U.S. patent application number 17/158911 was filed with the patent office on 2022-07-28 for content based related view recommendations.
The applicant listed for this patent is Tableau Software, LLC. Invention is credited to Eric Roy Brochu, Kazem Jahanbakhsh, Xiangbo Mao, Connie Hong-Ying Wong.
Application Number | 20220237229 17/158911 |
Document ID | / |
Family ID | 1000005372790 |
Filed Date | 2022-07-28 |
United States Patent
Application |
20220237229 |
Kind Code |
A1 |
Wong; Connie Hong-Ying ; et
al. |
July 28, 2022 |
CONTENT BASED RELATED VIEW RECOMMENDATIONS
Abstract
Embodiments are directed to managing visualizations of data. A
visualization associated with one or more community visualizations
that are associated with an organization may be displayed. A
recommendation score may be generated for each of the community
visualizations based on a comparison of meta-data fields. Top
ranked community visualizations may be determined based on the
recommendation score associated with each of the community
visualizations. An influence score may be determined the meta-data
fields based on a proportion of a value each meta-data field
contributes to the recommendation score of a corresponding top
ranked community visualization. A report that includes a rank
ordered list of the top ranked community visualizations and the top
ranked other meta-data fields may be provided to the user.
Inventors: |
Wong; Connie Hong-Ying;
(Vancouver, CA) ; Mao; Xiangbo; (Vancouver,
CA) ; Jahanbakhsh; Kazem; (Vancouver, CA) ;
Brochu; Eric Roy; (Vancouver, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Tableau Software, LLC |
Seattle |
WA |
US |
|
|
Family ID: |
1000005372790 |
Appl. No.: |
17/158911 |
Filed: |
January 26, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/909 20190101;
G06F 16/904 20190101; G06F 16/908 20190101; G06N 20/00 20190101;
G06F 16/90332 20190101; G06F 16/9035 20190101 |
International
Class: |
G06F 16/904 20060101
G06F016/904; G06F 16/908 20060101 G06F016/908; G06F 16/909 20060101
G06F016/909; G06F 16/9035 20060101 G06F016/9035; G06N 20/00
20060101 G06N020/00; G06F 16/9032 20060101 G06F016/9032 |
Claims
1. A method for managing visualizations of data using one or more
processors that execute instructions to perform actions,
comprising: displaying a visualization associated with one or more
community visualizations that are associated with an organization,
wherein a user is associated with the organization; determining one
or more fields of meta-data associated with the displayed
visualization, wherein the one or more meta-data fields are hidden
from view in the displayed visualization; generating a
recommendation score for each of the community visualizations based
on a recommendation model and the one or more meta-data fields,
wherein the recommendation score is based on a comparison of the
one or more meta-data fields to one or more other meta-data fields
associated with the one or more community visualizations;
determining a top ranking of the one or more community
visualizations, wherein the top ranking is based on the
recommendation score associated with each of the one or more
community visualizations; determining an influence score for each
of the one or more other meta-data fields based on a proportion of
a value each other meta-data field contributes to the
recommendation score of a corresponding top ranked community
visualization, wherein one or more top ranked other meta-data
fields for each top ranked community visualization are based on the
influence score for each of the one or more other meta-data fields;
and providing a report that includes a rank ordered list of the top
ranked community visualizations and the one or more top ranked
other meta-data fields.
2. The method of claim 1, wherein determining the one or more
meta-data fields, further comprises, determining the one or more
meta-data fields from a plurality of meta-data fields based on one
or more of a filter or a rule included in the recommendation model,
wherein the plurality of meta-data fields include one or more of an
author name, caption, visualization name, column name, table_name,
data source name, author role, author location, author
organization, reference to another table, data model object name,
creation date, or last-accessed date.
3. The method of claim 1, further comprising: providing one or more
sub-models that include one or more of, one or more heuristics, one
or more trained machine learning models, or one or more filters,
wherein the one or more sub-models are included in the
recommendation model; generating one or more partial scores based
on the one or more sub-models, the one or more meta-data fields,
and the one or more other meta-data fields; and determining the
recommendation score for each community visualization based on a
combination of the one or more partial scores, wherein the
combination is provided by the recommendation model.
4. The method of claim 1, further comprising: monitoring one or
more actions of the user that are associated with the one or more
top ranked community visualizations; storing information associated
with the one or more actions in a data store; and updating the
recommendation model based on the information stored in the data
store.
5. The method of claim 1, further comprising, associating a
narrative with each top ranked community visualization based on its
top ranked other meta-data fields, wherein the narrative includes a
natural language explanation for a rank of each top ranked
community visualization based on the one or more top ranked other
meta-data fields associated with each top ranked community
visualization.
6. The method of claim 1, wherein determining the influence score
for each of the one or more other meta-data fields, further
comprises, employing one or more influence models included in the
recommendation model to determine the influence score based on one
or more of one or more dominant topics determine by a topic model,
one or more counts of the one or more other meta-data fields that
include common values, or a magnitude of change to the
recommendation score and a temporary recommendation score that is
generated based on a temporary omission of one of the one or more
other meta-data fields from the generation of the temporary
recommendation score, wherein the influence score for each other
meta-data field is based on the magnitude of change that
corresponds to its omission.
7. The method of claim 1, wherein generating the recommendation
score for each of the community visualizations based on the
recommendation model and the one or more meta-data fields, further
comprises, employing one or more machine learning actions to
generate the recommendation score, wherein the one or more machine
learning actions include one or more of Latent Semantic Analysis
(LSA), Factorization Machines (FM), Cosine-similarity, Gradient
Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document
Frequency (TF-IDF), or discriminant analysis.
8. A processor readable non-transitory storage media that includes
instructions for managing visualizations of data, wherein execution
of the instructions by one or more processors, performs actions,
comprising: displaying a visualization associated with one or more
community visualizations that are associated with an organization,
wherein a user is associated with the organization; determining one
or more fields of meta-data associated with the displayed
visualization, wherein the one or more meta-data fields are hidden
from view in the displayed visualization; generating a
recommendation score for each of the community visualizations based
on a recommendation model and the one or more meta-data fields,
wherein the recommendation score is based on a comparison of the
one or more meta-data fields to one or more other meta-data fields
associated with the one or more community visualizations;
determining a top ranking of the one or more community
visualizations, wherein the top ranking is based on the
recommendation score associated with each of the one or more
community visualizations; determining an influence score for each
of the one or more other meta-data fields based on a proportion of
a value each other meta-data field contributes to the
recommendation score of a corresponding top ranked community
visualization, wherein one or more top ranked other meta-data
fields for each top ranked community visualization are based on the
influence score for each of the one or more other meta-data fields;
and providing a report that includes a rank ordered list of the top
ranked community visualizations and the one or more top ranked
other meta-data fields.
9. The media of claim 8, wherein determining the one or more
meta-data fields, further comprises, determining the one or more
meta-data fields from a plurality of meta-data fields based on one
or more of a filter or a rule included in the recommendation model,
wherein the plurality of meta-data fields include one or more of an
author name, caption, visualization name, column name, table_name,
data source name, author role, author location, author
organization, reference to another table, data model object name,
creation date, or last-accessed date.
10. The media of claim 8, further comprising: providing one or more
sub-models that include one or more of, one or more heuristics, one
or more trained machine learning models, or one or more filters,
wherein the one or more sub-models are included in the
recommendation model; generating one or more partial scores based
on the one or more sub-models, the one or more meta-data fields,
and the one or more other meta-data fields; and determining the
recommendation score for each community visualization based on a
combination of the one or more partial scores, wherein the
combination is provided by the recommendation model.
11. The media of claim 8, further comprising: monitoring one or
more actions of the user that are associated with the one or more
top ranked community visualizations; storing information associated
with the one or more actions in a data store; and updating the
recommendation model based on the information stored in the data
store.
12. The media of claim 8, further comprising, associating a
narrative with each top ranked community visualization based on its
top ranked other meta-data fields, wherein the narrative includes a
natural language explanation for a rank of each top ranked
community visualization based on the one or more top ranked other
meta-data fields associated with each top ranked community
visualization.
13. The media of claim 8, wherein determining the influence score
for each of the one or more other meta-data fields, further
comprises, employing one or more influence models included in the
recommendation model to determine the influence score based on one
or more of one or more dominant topics determine by a topic model,
one or more counts of the one or more other meta-data fields that
include common values, or a magnitude of change to the
recommendation score and a temporary recommendation score that is
generated based on a temporary omission of one of the one or more
other meta-data fields from the generation of the temporary
recommendation score, wherein the influence score for each other
meta-data field is based on the magnitude of change that
corresponds to its omission.
14. The media of claim 8, wherein generating the recommendation
score for each of the community visualizations based on the
recommendation model and the one or more meta-data fields, further
comprises, employing one or more machine learning actions to
generate the recommendation score, wherein the one or more machine
learning actions include one or more of Latent Semantic Analysis
(LSA), Factorization Machines (FM), Cosine-similarity, Gradient
Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document
Frequency (TF-IDF), or discriminant analysis.
15. A system for managing visualizations of data: a network
computer, comprising: a memory that stores at least instructions;
and one or more processors that execute instructions that perform
actions, including: displaying a visualization associated with one
or more community visualizations that are associated with an
organization, wherein a user is associated with the organization;
determining one or more fields of meta-data associated with the
displayed visualization, wherein the one or more meta-data fields
are hidden from view in the displayed visualization; generating a
recommendation score for each of the community visualizations based
on a recommendation model and the one or more meta-data fields,
wherein the recommendation score is based on a comparison of the
one or more meta-data fields to one or more other meta-data fields
associated with the one or more community visualizations;
determining a top ranking of the one or more community
visualizations, wherein the top ranking is based on the
recommendation score associated with each of the one or more
community visualizations; determining an influence score for each
of the one or more other meta-data fields based on a proportion of
a value each other meta-data field contributes to the
recommendation score of a corresponding top ranked community
visualization, wherein one or more top ranked other meta-data
fields for each top ranked community visualization are based on the
influence score for each of the one or more other meta-data fields;
and providing a report that includes a rank ordered list of the top
ranked community visualizations and the one or more top ranked
other meta-data fields; and a client computer, comprising: a memory
that stores at least instructions; and one or more processors that
execute instructions that perform actions, including: receiving the
report.
16. The system of claim 15, wherein determining the one or more
meta-data fields, further comprises, determining the one or more
meta-data fields from a plurality of meta-data fields based on one
or more of a filter or a rule included in the recommendation model,
wherein the plurality of meta-data fields include one or more of an
author name, caption, visualization name, column name, table_name,
data source name, author role, author location, author
organization, reference to another table, data model object name,
creation date, or last-accessed date.
17. The system of claim 15, wherein the one or more processors of
the network computer execute instructions that perform actions,
further comprising: providing one or more sub-models that include
one or more of, one or more heuristics, one or more trained machine
learning models, or one or more filters, wherein the one or more
sub-models are included in the recommendation model; generating one
or more partial scores based on the one or more sub-models, the one
or more meta-data fields, and the one or more other meta-data
fields; and determining the recommendation score for each community
visualization based on a combination of the one or more partial
scores, wherein the combination is provided by the recommendation
model.
18. The system of claim 15, wherein the one or more processors of
the network computer execute instructions that perform actions,
further comprising: monitoring one or more actions of the user that
are associated with the one or more top ranked community
visualizations; storing information associated with the one or more
actions in a data store; and updating the recommendation model
based on the information stored in the data store.
19. The system of claim 15, wherein the one or more processors of
the network computer execute instructions that perform actions,
further comprising, associating a narrative with each top ranked
community visualization based on its top ranked other meta-data
fields, wherein the narrative includes a natural language
explanation for a rank of each top ranked community visualization
based on the one or more top ranked other meta-data fields
associated with each top ranked community visualization.
20. The system of claim 15, wherein determining the influence score
for each of the one or more other meta-data fields, further
comprises, employing one or more influence models included in the
recommendation model to determine the influence score based on one
or more of one or more dominant topics determine by a topic model,
one or more counts of the one or more other meta-data fields that
include common values, or a magnitude of change to the
recommendation score and a temporary recommendation score that is
generated based on a temporary omission of one of the one or more
other meta-data fields from the generation of the temporary
recommendation score, wherein the influence score for each other
meta-data field is based on the magnitude of change that
corresponds to its omission.
21. The system of claim 15, wherein generating the recommendation
score for each of the community visualizations based on the
recommendation model and the one or more meta-data fields, further
comprises, employing one or more machine learning actions to
generate the recommendation score, wherein the one or more machine
learning actions include one or more of Latent Semantic Analysis
(LSA), Factorization Machines (FM), Cosine-similarity, Gradient
Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document
Frequency (TF-IDF), or discriminant analysis.
22. A network computer for managing visualizations of data,
comprising: a memory that stores at least instructions; and one or
more processors that execute instructions that perform actions,
including: displaying a visualization associated with one or more
community visualizations that are associated with an organization,
wherein a user is associated with the organization; determining one
or more fields of meta-data associated with the displayed
visualization, wherein the one or more meta-data fields are hidden
from view in the displayed visualization; generating a
recommendation score for each of the community visualizations based
on a recommendation model and the one or more meta-data fields,
wherein the recommendation score is based on a comparison of the
one or more meta-data fields to one or more other meta-data fields
associated with the one or more community visualizations;
determining a top ranking of the one or more community
visualizations, wherein the top ranking is based on the
recommendation score associated with each of the one or more
community visualizations; determining an influence score for each
of the one or more other meta-data fields based on a proportion of
a value each other meta-data field contributes to the
recommendation score of a corresponding top ranked community
visualization, wherein one or more top ranked other meta-data
fields for each top ranked community visualization are based on the
influence score for each of the one or more other meta-data fields;
and providing a report that includes a rank ordered list of the top
ranked community visualizations and the one or more top ranked
other meta-data fields.
23. The network computer of claim 22, wherein determining the one
or more meta-data fields, further comprises, determining the one or
more meta-data fields from a plurality of meta-data fields based on
one or more of a filter or a rule included in the recommendation
model, wherein the plurality of meta-data fields include one or
more of an author name, caption, visualization name, column name,
table_name, data source name, author role, author location, author
organization, reference to another table, data model object name,
creation date, or last-accessed date.
24. The network computer of claim 22, wherein the one or more
processors execute instructions that perform actions, further
comprising: providing one or more sub-models that include one or
more of, one or more heuristics, one or more trained machine
learning models, or one or more filters, wherein the one or more
sub-models are included in the recommendation model; generating one
or more partial scores based on the one or more sub-models, the one
or more meta-data fields, and the one or more other meta-data
fields; and determining the recommendation score for each community
visualization based on a combination of the one or more partial
scores, wherein the combination is provided by the recommendation
model.
25. The network computer of claim 22, wherein the one or more
processors execute instructions that perform actions, further
comprising: monitoring one or more actions of the user that are
associated with the one or more top ranked community
visualizations; storing information associated with the one or more
actions in a data store; and updating the recommendation model
based on the information stored in the data store.
26. The network computer of claim 22, wherein the one or more
processors execute instructions that perform actions, further
comprising, associating a narrative with each top ranked community
visualization based on its top ranked other meta-data fields,
wherein the narrative includes a natural language explanation for a
rank of each top ranked community visualization based on the one or
more top ranked other meta-data fields associated with each top
ranked community visualization.
27. The network computer of claim 22, wherein determining the
influence score for each of the one or more other meta-data fields,
further comprises, employing one or more influence models included
in the recommendation model to determine the influence score based
on one or more of one or more dominant topics determine by a topic
model, one or more counts of the one or more other meta-data fields
that include common values, or a magnitude of change to the
recommendation score and a temporary recommendation score that is
generated based on a temporary omission of one of the one or more
other meta-data fields from the generation of the temporary
recommendation score, wherein the influence score for each other
meta-data field is based on the magnitude of change that
corresponds to its omission.
28. The network computer of claim 22, wherein generating the
recommendation score for each of the community visualizations based
on the recommendation model and the one or more meta-data fields,
further comprises, employing one or more machine learning actions
to generate the recommendation score, wherein the one or more
machine learning actions include one or more of Latent Semantic
Analysis (LSA), Factorization Machines (FM), Cosine-similarity,
Gradient Boosting Decision Trees (GBDTs), Term Frequency-Inverse
Document Frequency (TF-IDF), or discriminant analysis.
Description
TECHNICAL FIELD
[0001] The present innovations relate generally to data
visualization, and more particularly, but not exclusively to,
recommendation of visualizations to users.
BACKGROUND
[0002] Organizations are generating and collecting an ever
increasing amount of data. This data may be associated with
disparate parts of the organization, such as, consumer activity,
manufacturing activity, customer service, server logs, or the like.
For various reasons, it may be inconvenient for such organizations
to effectively utilize their vast collections of data. In some
cases the quantity of data may make it difficult to effectively
utilize the collected data to improve business practices. In some
cases, organizations employ various tools to generate
visualizations of the some or all of their data. Employing
visualizations to represent this data may enable organizations to
improve their understanding of critical business operations and
help them monitor key performance indicators. However, in some
cases, organizations may have many visualizations that may be used
for a variety of purposes. In some cases, selecting the appropriate
visualizations for a given analysis may be challenging for
non-authoring users who may be unfamiliar with the motivations or
assumptions of the author who created the visualization. Likewise,
in some cases, organizations may have many useful or popular
visualizations that users may be unaware of Thus, is with respect
to these considerations and others that the present innovations
have been made.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Non-limiting and non-exhaustive embodiments of the present
innovations are described with reference to the following drawings.
In the drawings, like reference numerals refer to like parts
throughout the various figures unless otherwise specified. For a
better understanding of the described innovations, reference will
be made to the following Detailed Description of Various
Embodiments, which is to be read in association with the
accompanying drawings, wherein:
[0004] FIG. 1 illustrates a system environment in which various
embodiments may be implemented;
[0005] FIG. 2 illustrates a schematic embodiment of a client
computer;
[0006] FIG. 3 illustrates a schematic embodiment of a network
computer;
[0007] FIG. 4 illustrates a logical architecture of a system for
content based related view recommendations in accordance with one
or more of the various embodiments;
[0008] FIG. 5 illustrates a logical schematic of a portion of a
system showing some metadata for content based related view
recommendations in accordance with one or more of the various
embodiments;
[0009] FIG. 6 illustrates a logical representation of a portion of
a user interface for content based related view recommendations in
accordance with one or more of the various embodiments;
[0010] FIG. 7 illustrates an overview flowchart for a process for
content based related view recommendations in accordance with one
or more of the various embodiments;
[0011] FIG. 8 illustrates a flowchart for a process for generating
recommendation models for content based related view
recommendations in accordance with one or more of the various
embodiments;
[0012] FIG. 9 illustrates a flowchart for a process for content
based related view recommendations in accordance with one or more
of the various embodiments;
[0013] FIG. 10 illustrates a flowchart for a process for content
based related view recommendations in accordance with one or more
of the various embodiments;
[0014] FIG. 11 illustrates a flowchart for a process for providing
initial recommendation models based on a baseline model in
accordance with one or more of the various embodiments;
[0015] FIG. 12 illustrates a flowchart for a process for evaluating
metadata fields to provide a summary narrative associated the
recommended visualizations in accordance with one or more of the
various embodiments; and
[0016] FIG. 13 illustrates a flowchart for a process for evaluating
metadata fields influence between selected recommended
visualizations in accordance with one or more of the various
embodiments.
DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS
[0017] Various embodiments now will be described more fully
hereinafter with reference to the accompanying drawings, which form
a part hereof, and which show, by way of illustration, specific
exemplary embodiments by which the innovations herein may be
practiced. The embodiments may, however, be embodied in many
different forms and should not be construed as limited to the
embodiments set forth herein; rather, these embodiments are
provided so that this disclosure will be thorough and complete, and
will fully convey the scope of the embodiments to those skilled in
the art. Among other things, the various embodiments may be
methods, systems, media or devices. Accordingly, the various
embodiments may take the form of an entirely hardware embodiment,
an entirely software embodiment or an embodiment combining software
and hardware aspects. The following detailed description is,
therefore, not to be taken in a limiting sense.
[0018] Throughout the specification and claims, the following terms
take the meanings explicitly associated herein, unless the context
clearly dictates otherwise. The phrases "in one or more of the
various embodiments," "in some embodiments," "for some
embodiments," "in one embodiment" as used herein do not necessarily
refer to the same embodiments, though they may. Furthermore, the
phrases "in one or more of the various embodiments," "in some
embodiments," "for some embodiments," "in one embodiment" as used
herein do not necessarily refer to different embodiments, although
they may. Thus, as described below, various embodiments may be
readily combined, without departing from the scope or spirit of the
innovations described herein.
[0019] In addition, as used herein, the term "or" is an inclusive
"or" operator, and is equivalent to the term "and/or," unless the
context clearly dictates otherwise. The term "based on" is not
exclusive and allows for being based on additional factors not
described, unless the context clearly dictates otherwise. In
addition, throughout the specification, the meaning of "a," "an,"
and "the" include plural references. The meaning of "in" includes
"in" and "on."
[0020] For example embodiments, the following terms are also used
herein according to the corresponding meaning, unless the context
clearly dictates otherwise.
[0021] As used herein the term, "engine" refers to logic embodied
in hardware or software instructions, which can be written in a
programming language, such as C, C++, Objective-C, COBOL, Java.TM.,
PHP, Perl, JavaScript, Ruby, VBScript, Microsoft.NET.TM. languages
such as C#, or the like. An engine may be compiled into executable
programs or written in interpreted programming languages. Software
engines may be callable from other engines or from themselves.
Engines described herein refer to one or more logical modules that
can be merged with other engines or applications, or can be divided
into sub-engines. The engines can be stored in non-transitory
computer-readable medium or computer storage device and be stored
on and executed by one or more general purpose computers, thus
creating a special purpose computer configured to provide the
engine.
[0022] As used herein, the term "data source" refers to databases,
applications, services, file systems, or the like, that store or
provide information for an organization. Examples of data sources
may include, RDBMS databases, graph databases, spreadsheets, file
systems, document management systems, local or remote data streams,
or the like. In some cases, data sources are organized around one
or more tables or table-like structure. In other cases, data
sources be organized as a graph or graph-like structure.
[0023] As used herein the term "data model" refers to one or more
data structures that provide a representation of an underlying data
source. In some cases, data models may provide views of a data
source for particular applications. Data models may be considered
views or interfaces to the underlying data source. In some cases,
data models may map directly to a data source (e.g., practically a
logical pass through). Also, in some cases, data models may be
provided by a data source. In some circumstances, data models may
be considered interfaces to data sources. Data models enable
organizations to organize or present information from data sources
in ways that may be more convenient, more meaningful (e.g, easier
to reason about), safer, or the like.
[0024] As used herein the term "data object" refers to one or more
entities or data structures that comprise data models. In some
cases, data objects may be considered portions of the data model.
Data objects may represent individual instances of items or classes
or kinds of items.
[0025] As used herein the term "panel" refers to region within a
graphical user interface (GUI) that has a defined geometry (e.g.,
x, y, z-order) within the GUI. Panels may be arranged to display
information to users or to host one or more interactive controls.
The geometry or styles associated with panels may be defined using
configuration information, including dynamic rules. Also, in some
cases, users may be enabled to perform actions on one or more
panels, such as, moving, showing, hiding, re-sizing, re-ordering,
or the like.
[0026] As user herein the "visualization model" refers to one or
more data structures that represent one or more representations of
a data model that may be suitable for use in a visualization that
is displayed on one or more hardware displays. Visualization models
may define styling or user interface features that may be made
available to non-authoring user.
[0027] As used herein the term "display object" refers to one or
more data structures that comprise visualization models. In some
cases, display objects may be considered portions of the
visualization model. Display objects may represent individual
instances of items or entire classes or kinds of items that may be
displayed in a visualization. In some embodiments, display objects
may be considered or referred to as views because they provide a
view of some portion of the data model.
[0028] As used herein the term "recommendation model" refers to one
or more data structures that include machine learning based models
that may be arranged to predict visualizations for users. In some
cases, there may be different types of recommendation models that
may be based on different types of machine learning. Likewise, in
some embodiments, different recommendation models may be arranged
for recommending visualizations based on different criteria or for
different purposes. In some cases, recommendation models may
include one or more heuristics, filters, or the like, that work in
conjunction with one or more machine learning sub-models.
[0029] As used herein the term "baseline model" refers to a
recommendation model that has been trained or tuned based on
training data associated with public or common usage history of a
general population a users rather than users associated with the
organization. Baseline models may be used to bootstrap initial
recommendation models that may be used until sufficient training
data is collected to train personalized models for users in an
individual organization.
[0030] As used herein the term "user profile" refers to a data
structure that includes information or data that is based on or
associated with one or more characteristics of an individual user.
For example, user profiles may include information that represents
information, such as, user identity, group membership, role, access
rights, previous activity, preferred visualizations, user
preferences, or the like. In some cases, user profiles may include
references or pointers to additional information including
historical activity logs, telemetry information, or the like. In
some embodiments, some or all values included in user profiles may
be normalized, weighted, curved, shaped, or the like, to enable
modeling engines to train recommendation models or for use by
recommendation engines to recommend visualizations based on user
profiles.
[0031] As used herein the terms "visualization metadata,"
"meta-data fields," or "metadata" refer to values provided from
fields associated with visualizations that may describe various
characteristics of a visualization or associated with a
visualization that may be separate or distinguishable from the
visualization as viewed by users or authors. Meta-data may be
important for describing one or more characteristics of the
visualization but they are not an authored portion of the
visualization. Though, in some cases, information displayed in a
visualization may include information derived from meta-data.
However, meta-data may be associated with visualization whether it
is displayed or otherwise visible in a visualization. Meta-data
comes in many forms and several examples are discussed herein. A
few examples of meta-data may include, author name, table names
(from data sources), column names (from data source tables), or the
like.
[0032] As used herein the term "community visualization" refers to
one or more visualizations that may be associated with an
organization or other larger public community. Community
visualizations may be considered other visualizations that a user
has permission to access or view.
[0033] In some cases, some community visualizations may be
visualizations generated by the same or other users in the
organization. In other cases, one or more community visualizations
may be arranged to visualization that may be in the public domain
or otherwise accessible to the public. For clarity and brevity, the
different categories of community visualizations are simply
referred to as community visualizations unless the context clearly
indicates otherwise. Recommended visualizations may be provided
from among community visualizations.
[0034] As used herein the term "recommendation score" refers a
score, metric, or measurement associated with a visualization that
indicates a quality of the recommendation as per the criteria of
the recommendation model providing the score. In most case, a
recommendation score may be a combination of one or more partial
scores that may be combined into one value.
[0035] As used herein the term "influence score" refers to a score,
metric, or measurement associated with individual metadata fields
associated with a recommended visualization. The influence score
represent how much the individual metadata fields contributed to
the recommendation score of a recommended visualization. Influence
scores help answer the question of why a particular visualization
is recommended.
[0036] As used the term "meta-attribute" refers to data structures
used to represent metadata fields at least employed for generating
influence scores for metadata fields. meta-attributes combine a
metadata field and its value into one value or one tuple. For
example, if metadata field `tablename` has the value `customers`
its corresponding meta-attribute may be `tablename customers` or
(`tablename`, `customers`).
[0037] As used herein the term "anchor visualization" refers a
visualization that a user may be viewing or otherwise interacting
with. Typically, user may employ one or more user interface
features to select or `focus` on one or more visualizations, these
visualizations may be considered anchor visualizations.
[0038] As used herein the term "configuration information" refers
to information that may include rule based policies, pattern
matching, scripts (e.g., computer readable instructions), or the
like, that may be provided from various sources, including,
configuration files, databases, user input, built-in defaults, or
the like, or combination thereof.
[0039] The following briefly describes embodiments of the
innovations herein to provide a basic understanding of some aspects
of the invention. This brief description is not intended as an
extensive overview. It is not intended to fully identify key or
critical elements, or to fully delineate or otherwise narrow the
scope. Its purpose is merely to present some concepts in a
simplified form as a prelude to the more detailed description that
is presented later.
[0040] Briefly stated, various embodiments are directed to managing
visualizations. In one or more of the various embodiments, a
visualization associated with one or more community visualizations
that are associated with an organization may be displayed for a
user may be associated with the organization.
[0041] In one or more of the various embodiments, one or more
fields of meta-data associated with the displayed visualization may
be determined such that the one or more meta-data fields may be
hidden from view in the displayed visualization. In some
embodiments, determining the one or more meta-data fields may
include determining the one or more meta-data fields from a
plurality of meta-data fields based on one or more of a filter or a
rule included in the recommendation model, wherein the plurality of
meta-data fields include one or more of an author name, caption,
visualization name, column name, table name, data source name,
author role, author location, author organization, reference to
another table, data model object name, creation date, last-accessed
date, or the like.
[0042] In one or more of the various embodiments, a recommendation
score may be generated for each of the community visualizations
based on a recommendation model and the one or more meta-data
fields such that the recommendation score may be based on a
comparison of the one or more meta-data fields to one or more other
meta-data fields associated with the one or more community
visualizations. In some embodiments, generating the recommendation
score for each of the community visualizations fields may include
employing one or more machine learning actions to generate the
recommendation score, wherein the one or more machine learning
actions include one or more of Latent Semantic Analysis (LSA),
Factorization Machines (FM), Cosine-similarity, Gradient Boosting
Decision Trees (GBDTs), Term Frequency-Inverse Document Frequency
(TF-IDF), discriminant analysis, or the like.
[0043] In one or more of the various embodiments, a top ranking of
the one or more community visualizations may be determined such
that the top ranking may be based on the recommendation score
associated with each of the one or more community
visualizations.
[0044] In one or more of the various embodiments, an influence
score for each of the one or more other meta-data fields may be
determined based on a proportion of a value each other meta-data
field contributes to the recommendation score of a corresponding
top ranked community visualization such that one or more top ranked
other meta-data fields for each top ranked community visualization
may be based on the influence score for each of the one or more
other meta-data fields. In some embodiments, determining the
influence score for each of the one or more other meta-data fields
may include employing one or more influence models included in the
recommendation model to determine the influence score based on one
or more of one or more dominant topics determine by a topic model,
one or more counts of the one or more other meta-data fields that
include common values, or a magnitude of change to the
recommendation score and a temporary recommendation score that is
generated based on a temporary omission of one of the one or more
other meta-data fields from the generation of the temporary
recommendation score such that the influence score for each other
meta-data field may be based on the magnitude of change that
corresponds to its omission.
[0045] In one or more of the various embodiments, a report that
includes a rank ordered list of the top ranked community
visualizations and the one or more top ranked other meta-data
fields may be provided to the user.
[0046] In one or more of the various embodiments, one or more
sub-models that include one or more of, one or more heuristics, one
or more trained machine learning models, or one or more filters may
be provided such that the one or more sub-models may be included in
the recommendation model. In some embodiments, one or more partial
scores may be generated based on the one or more sub-models, the
one or more meta-data fields or the one or more other meta-data
fields. And, in some embodiments, the recommendation score for each
community visualization may be determined based on a combination of
the one or more partial scores such that the combination may be
provided by the recommendation model.
[0047] In one or more of the various embodiments, one or more
actions of the user that may be associated with the one or more top
ranked community visualizations may be monitored. In some
embodiments, information associated with the one or more actions
may be stored in a data store. And, in some embodiments, the
recommendation model may be updated based on the information stored
in the data store.
[0048] In one or more of the various embodiments, a narrative may
be associated with each top ranked community visualization based on
its top ranked other meta-data fields such that the narrative
includes a natural language explanation for a rank of each top
ranked community visualization based on the one or more top ranked
other meta-data fields associated with each top ranked community
visualization.
Illustrated Operating Environment
[0049] FIG. 1 shows components of one embodiment of an environment
in which embodiments of the innovations may be practiced. Not all
of the components may be required to practice these innovations,
and variations in the arrangement and type of the components may be
made without departing from the spirit or scope of these
innovations. As shown, system 100 of FIG. 1 includes local area
networks (LANs)/wide area networks (WANs)-(network) 110, wireless
network 108, client computers 102-105, visualization server
computer 116, data source server computer 118, or the like.
[0050] At least one embodiment of client computers 102-105 is
described in more detail below in conjunction with FIG. 2. In one
embodiment, at least some of client computers 102-105 may operate
over one or more wired or wireless networks, such as networks 108,
or 110. Generally, client computers 102-105 may include virtually
any computer capable of communicating over a network to send and
receive information, perform various online activities, offline
actions, or the like. In one embodiment, one or more of client
computers 102-105 may be configured to operate within a business or
other entity to perform a variety of services for the business or
other entity. For example, client computers 102-105 may be
configured to operate as a web server, firewall, client
application, media player, mobile telephone, game console, desktop
computer, or the like. However, client computers 102-105 are not
constrained to these services and may also be employed, for
example, as for end-user computing in other embodiments. It should
be recognized that more or less client computers (as shown in FIG.
1) may be included within a system such as described herein, and
embodiments are therefore not constrained by the number or type of
client computers employed.
[0051] Computers that may operate as client computer 102 may
include computers that typically connect using a wired or wireless
communications medium such as personal computers, multiprocessor
systems, microprocessor-based or programmable electronic devices,
network PCs, or the like. In some embodiments, client computers
102-105 may include virtually any portable computer capable of
connecting to another computer and receiving information such as,
laptop computer 103, mobile computer 104, tablet computers 105, or
the like. However, portable computers are not so limited and may
also include other portable computers such as cellular telephones,
display pagers, radio frequency (RF) devices, infrared (IR)
devices, Personal Digital Assistants (PDAs), handheld computers,
wearable computers, integrated devices combining one or more of the
preceding computers, or the like. As such, client computers 102-105
typically range widely in terms of capabilities and features.
Moreover, client computers 102-105 may access various computing
applications, including a browser, or other web-based
application.
[0052] A web-enabled client computer may include a browser
application that is configured to send requests and receive
responses over the web. The browser application may be configured
to receive and display graphics, text, multimedia, and the like,
employing virtually any web-based language. In one embodiment, the
browser application is enabled to employ JavaScript, HyperText
Markup Language (HTML), eXtensible Markup Language (XML),
JavaScript Object Notation (JSON), Cascading Style Sheets (CSS), or
the like, or combination thereof, to display and send a message. In
one embodiment, a user of the client computer may employ the
browser application to perform various activities over a network
(online). However, another application may also be used to perform
various online activities.
[0053] Client computers 102-105 also may include at least one other
client application that is configured to receive or send content
between another computer. The client application may include a
capability to send or receive content, or the like. The client
application may further provide information that identifies itself,
including a type, capability, name, and the like. In one
embodiment, client computers 102-105 may uniquely identify
themselves through any of a variety of mechanisms, including an
Internet Protocol (IP) address, a phone number, Mobile
Identification Number (MIN), an electronic serial number (ESN), a
client certificate, or other device identifier. Such information
may be provided in one or more network packets, or the like, sent
between other client computers, visualization server computer 116,
data source server computer 118, or other computers.
[0054] Client computers 102-105 may further be configured to
include a client application that enables an end-user to log into
an end-user account that may be managed by another computer, such
as visualization server computer 116, data source server computer
118, or the like. Such an end-user account, in one non-limiting
example, may be configured to enable the end-user to manage one or
more online activities, including in one non-limiting example,
project management, software development, system administration,
configuration management, search activities, social networking
activities, browse various websites, communicate with other users,
or the like. Also, client computers may be arranged to enable users
to display reports, interactive user-interfaces, or results
provided by visualization server computer 116, data source server
computer 118, or the like.
[0055] Wireless network 108 is configured to couple client
computers 103-105 and its components with network 110. Wireless
network 108 may include any of a variety of wireless sub-networks
that may further overlay stand-alone ad-hoc networks, and the like,
to provide an infrastructure-oriented connection for client
computers 103-105. Such sub-networks may include mesh networks,
Wireless LAN (WLAN) networks, cellular networks, and the like. In
one embodiment, the system may include more than one wireless
network.
[0056] Wireless network 108 may further include an autonomous
system of terminals, gateways, routers, and the like connected by
wireless radio links, and the like. These connectors may be
configured to move freely and randomly and organize themselves
arbitrarily, such that the topology of wireless network 108 may
change rapidly.
[0057] Wireless network 108 may further employ a plurality of
access technologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G)
generation radio access for cellular systems, WLAN, Wireless Router
(WR) mesh, and the like. Access technologies such as 2G, 3G, 4G,
5G, and future access networks may enable wide area coverage for
mobile computers, such as client computers 103-105 with various
degrees of mobility. In one non-limiting example, wireless network
108 may enable a radio connection through a radio network access
such as Global System for Mobil communication (GSM), General Packet
Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), code
division multiple access (CDMA), time division multiple access
(TDMA), Wideband Code Division Multiple Access (WCDMA), High Speed
Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the
like. In essence, wireless network 108 may include virtually any
wireless communication mechanism by which information may travel
between client computers 103-105 and another computer, network, a
cloud-based network, a cloud instance, or the like.
[0058] Network 110 is configured to couple network computers with
other computers, including, visualization server computer 116, data
source server computer 118, client computers 102, and client
computers 103-105 through wireless network 108, or the like.
Network 110 is enabled to employ any form of computer readable
media for communicating information from one electronic device to
another. Also, network 110 can include the Internet in addition to
local area networks (LANs), wide area networks (WANs), direct
connections, such as through a universal serial bus (USB) port,
Ethernet port, other forms of computer-readable media, or any
combination thereof. On an interconnected set of LANs, including
those based on differing architectures and protocols, a router acts
as a link between LANs, enabling messages to be sent from one to
another. In addition, communication links within LANs typically
include twisted wire pair or coaxial cable, while communication
links between networks may utilize analog telephone lines, full or
fractional dedicated digital lines including T1, T2, T3, and T4, or
other carrier mechanisms including, for example, E-carriers,
Integrated Services Digital Networks (ISDNs), Digital Subscriber
Lines (DSLs), wireless links including satellite links, or other
communications links known to those skilled in the art. Moreover,
communication links may further employ any of a variety of digital
signaling technologies, including without limit, for example, DS-0,
DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.
Furthermore, remote computers and other related electronic devices
could be remotely connected to either LANs or WANs via a modem and
temporary telephone link. In one embodiment, network 110 may be
configured to transport information of an Internet Protocol
(IP).
[0059] Additionally, communication media typically embodies
computer readable instructions, data structures, program modules,
or other transport mechanism and includes any information
non-transitory delivery media or transitory delivery media. By way
of example, communication media includes wired media such as
twisted pair, coaxial cable, fiber optics, wave guides, and other
wired media and wireless media such as acoustic, RF, infrared, and
other wireless media.
[0060] Also, one embodiment of visualization server computer 116,
data source server computer 118 are described in more detail below
in conjunction with FIG. 3. Although FIG. 1 illustrates
visualization server computer 116, data source server computer 118,
or the like, each as a single computer, the innovations or
embodiments are not so limited. For example, one or more functions
of visualization server computer 116, data source server computer
118, or the like, may be distributed across one or more distinct
network computers. Moreover, in one or more embodiments,
visualization server computer 116, data source server computer 118
may be implemented using a plurality of network computers. Further,
in one or more of the various embodiments, visualization server
computer 116, data source server computer 118, or the like, may be
implemented using one or more cloud instances in one or more cloud
networks. Accordingly, these innovations and embodiments are not to
be construed as being limited to a single environment, and other
configurations, and other architectures are also envisaged.
Illustrative Client Computer
[0061] FIG. 2 shows one embodiment of client computer 200 that may
include many more or less components than those shown. Client
computer 200 may represent, for example, one or more embodiment of
mobile computers or client computers shown in FIG. 1.
[0062] Client computer 200 may include processor 202 in
communication with memory 204 via bus 228. Client computer 200 may
also include power supply 230, network interface 232, audio
interface 256, display 250, keypad 252, illuminator 254, video
interface 242, input/output interface 238, haptic interface 264,
global positioning systems (GPS) receiver 258, open air gesture
interface 260, temperature interface 262, camera(s) 240, projector
246, pointing device interface 266, processor-readable stationary
storage device 234, and processor-readable removable storage device
236. Client computer 200 may optionally communicate with a base
station (not shown), or directly with another computer. And in one
embodiment, although not shown, a gyroscope may be employed within
client computer 200 to measuring or maintaining an orientation of
client computer 200.
[0063] Power supply 230 may provide power to client computer 200. A
rechargeable or non-rechargeable battery may be used to provide
power. The power may also be provided by an external power source,
such as an AC adapter or a powered docking cradle that supplements
or recharges the battery.
[0064] Network interface 232 includes circuitry for coupling client
computer 200 to one or more networks, and is constructed for use
with one or more communication protocols and technologies
including, but not limited to, protocols and technologies that
implement any portion of the OSI model for mobile communication
(GSM), CDMA, time division multiple access (TDMA), UDP, TCP/IP,
SMS, MMS, GPRS, WAP, UWB, WiMax, SIP/RTP, GPRS, EDGE, WCDMA, LTE,
UMTS, OFDM, CDMA2000, EV-DO, HSDPA, or any of a variety of other
wireless communication protocols. Network interface 232 is
sometimes known as a transceiver, transceiving device, or network
interface card (MC).
[0065] Audio interface 256 may be arranged to produce and receive
audio signals such as the sound of a human voice. For example,
audio interface 256 may be coupled to a speaker and microphone (not
shown) to enable telecommunication with others or generate an audio
acknowledgment for some action. A microphone in audio interface 256
can also be used for input to or control of client computer 200,
e.g., using voice recognition, detecting touch based on sound, and
the like.
[0066] Display 250 may be a liquid crystal display (LCD), gas
plasma, electronic ink, light emitting diode (LED), Organic LED
(OLED) or any other type of light reflective or light transmissive
display that can be used with a computer. Display 250 may also
include a touch interface 244 arranged to receive input from an
object such as a stylus or a digit from a human hand, and may use
resistive, capacitive, surface acoustic wave (SAW), infrared,
radar, or other technologies to sense touch or gestures.
[0067] Projector 246 may be a remote handheld projector or an
integrated projector that is capable of projecting an image on a
remote wall or any other reflective object such as a remote
screen.
[0068] Video interface 242 may be arranged to capture video images,
such as a still photo, a video segment, an infrared video, or the
like. For example, video interface 242 may be coupled to a digital
video camera, a web-camera, or the like. Video interface 242 may
comprise a lens, an image sensor, and other electronics. Image
sensors may include a complementary metal-oxide-semiconductor
(CMOS) integrated circuit, charge-coupled device (CCD), or any
other integrated circuit for sensing light.
[0069] Keypad 252 may comprise any input device arranged to receive
input from a user. For example, keypad 252 may include a push
button numeric dial, or a keyboard. Keypad 252 may also include
command buttons that are associated with selecting and sending
images.
[0070] Illuminator 254 may provide a status indication or provide
light. Illuminator 254 may remain active for specific periods of
time or in response to event messages. For example, when
illuminator 254 is active, it may back-light the buttons on keypad
252 and stay on while the client computer is powered. Also,
illuminator 254 may back-light these buttons in various patterns
when particular actions are performed, such as dialing another
client computer. Illuminator 254 may also cause light sources
positioned within a transparent or translucent case of the client
computer to illuminate in response to actions.
[0071] Further, client computer 200 may also comprise hardware
security module (HSM) 268 for providing additional tamper resistant
safeguards for generating, storing or using security/cryptographic
information such as, keys, digital certificates, passwords,
passphrases, two-factor authentication information, or the like. In
some embodiments, hardware security module may be employed to
support one or more standard public key infrastructures (PKI), and
may be employed to generate, manage, or store keys pairs, or the
like. In some embodiments, HSM 268 may be a stand-alone computer,
in other cases, HSM 268 may be arranged as a hardware card that may
be added to a client computer.
[0072] Client computer 200 may also comprise input/output interface
238 for communicating with external peripheral devices or other
computers such as other client computers and network computers. The
peripheral devices may include an audio headset, virtual reality
headsets, display screen glasses, remote speaker system, remote
speaker and microphone system, and the like. Input/output interface
238 can utilize one or more technologies, such as Universal Serial
Bus (USB), Infrared, WiFi, WiMax, Bluetooth.TM., and the like.
[0073] Input/output interface 238 may also include one or more
sensors for determining geolocation information (e.g., GPS),
monitoring electrical power conditions (e.g., voltage sensors,
current sensors, frequency sensors, and so on), monitoring weather
(e.g., thermostats, barometers, anemometers, humidity detectors,
precipitation scales, or the like), or the like. Sensors may be one
or more hardware sensors that collect or measure data that is
external to client computer 200.
[0074] Haptic interface 264 may be arranged to provide tactile
feedback to a user of the client computer. For example, the haptic
interface 264 may be employed to vibrate client computer 200 in a
particular way when another user of a computer is calling.
Temperature interface 262 may be used to provide a temperature
measurement input or a temperature changing output to a user of
client computer 200. Open air gesture interface 260 may sense
physical gestures of a user of client computer 200, for example, by
using single or stereo video cameras, radar, a gyroscopic sensor
inside a computer held or worn by the user, or the like. Camera 240
may be used to track physical eye movements of a user of client
computer 200.
[0075] GPS transceiver 258 can determine the physical coordinates
of client computer 200 on the surface of the Earth, which typically
outputs a location as latitude and longitude values. GPS
transceiver 258 can also employ other geo-positioning mechanisms,
including, but not limited to, triangulation, assisted GPS (AGPS),
Enhanced Observed Time Difference (E-OTD), Cell Identifier (CI),
Service Area Identifier (SAI), Enhanced Timing Advance (ETA), Base
Station Subsystem (BSS), or the like, to further determine the
physical location of client computer 200 on the surface of the
Earth. It is understood that under different conditions, GPS
transceiver 258 can determine a physical location for client
computer 200. In one or more embodiments, however, client computer
200 may, through other components, provide other information that
may be employed to determine a physical location of the client
computer, including for example, a Media Access Control (MAC)
address, IP address, and the like.
[0076] In at least one of the various embodiments, applications,
such as, operating system 206, other client apps 224, web browser
226, or the like, may be arranged to employ geo-location
information to select one or more localization features, such as,
time zones, languages, currencies, calendar formatting, or the
like. Localization features may be used in display objects, data
models, data objects, user-interfaces, reports, as well as internal
processes or databases. In at least one of the various embodiments,
geo-location information used for selecting localization
information may be provided by GPS 258. Also, in some embodiments,
geolocation information may include information provided using one
or more geolocation protocols over the networks, such as, wireless
network 108 or network 111.
[0077] Human interface components can be peripheral devices that
are physically separate from client computer 200, allowing for
remote input or output to client computer 200. For example,
information routed as described here through human interface
components such as display 250 or keyboard 252 can instead be
routed through network interface 232 to appropriate human interface
components located remotely. Examples of human interface peripheral
components that may be remote include, but are not limited to,
audio devices, pointing devices, keypads, displays, cameras,
projectors, and the like. These peripheral components may
communicate over a Pico Network such as Bluetooth.TM., Zigbee.TM.
and the like. One non-limiting example of a client computer with
such peripheral human interface components is a wearable computer,
which might include a remote pico projector along with one or more
cameras that remotely communicate with a separately located client
computer to sense a user's gestures toward portions of an image
projected by the pico projector onto a reflected surface such as a
wall or the user's hand.
[0078] A client computer may include web browser application 226
that is configured to receive and to send web pages, web-based
messages, graphics, text, multimedia, and the like. The client
computer's browser application may employ virtually any programming
language, including a wireless application protocol messages (WAP),
and the like. In one or more embodiments, the browser application
is enabled to employ Handheld Device Markup Language (HDML),
Wireless Markup Language (WML), WMLScript, JavaScript, Standard
Generalized Markup Language (SGML), HyperText Markup Language
(HTML), eXtensible Markup Language (XML), HTMLS, and the like.
[0079] Memory 204 may include RAM, ROM, or other types of memory.
Memory 204 illustrates an example of computer-readable storage
media (devices) for storage of information such as
computer-readable instructions, data structures, program modules or
other data. Memory 204 may store BIOS 208 for controlling low-level
operation of client computer 200. The memory may also store
operating system 206 for controlling the operation of client
computer 200. It will be appreciated that this component may
include a general-purpose operating system such as a version of
UNIX, or Linux.RTM., or a specialized client computer communication
operating system such as Windows, iOS, macOS, or the like. The
operating system may include, or interface with a Java virtual
machine module that enables control of hardware components or
operating system operations via Java application programs.
[0080] Memory 204 may further include one or more data storage 210,
which can be utilized by client computer 200 to store, among other
things, applications 220 or other data. For example, data storage
210 may also be employed to store information that describes
various capabilities of client computer 200. The information may
then be provided to another device or computer based on any of a
variety of methods, including being sent as part of a header during
a communication, sent upon request, or the like. Data storage 210
may also be employed to store social networking information
including address books, buddy lists, aliases, user profile
information, or the like. Data storage 210 may further include
program code, data, algorithms, and the like, for use by a
processor, such as processor 202 to execute and perform actions. In
one embodiment, at least some of data storage 210 might also be
stored on another component of client computer 200, including, but
not limited to, non-transitory processor-readable removable storage
device 236, processor-readable stationary storage device 234, or
even external to the client computer.
[0081] Applications 220 may include computer executable
instructions which, when executed by client computer 200, transmit,
receive, or otherwise process instructions and data. Applications
220 may include, for example, client visualization engine 222,
other client applications 224, web browser 226, or the like. Client
computers may be arranged to exchange communications one or more
servers.
[0082] Other examples of application programs include calendars,
search programs, email client applications, IM applications, SMS
applications, Voice Over Internet Protocol (VOIP) applications,
contact managers, task managers, transcoders, database programs,
word processing programs, security applications, spreadsheet
programs, games, search programs, visualization applications, and
so forth.
[0083] Additionally, in one or more embodiments (not shown in the
figures), client computer 200 may include an embedded logic
hardware device instead of a CPU, such as, an Application Specific
Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA),
Programmable Array Logic (PAL), or the like, or combination
thereof. The embedded logic hardware device may directly execute
its embedded logic to perform actions. Also, in one or more
embodiments (not shown in the figures), client computer 200 may
include one or more hardware micro-controllers instead of CPUs. In
one or more embodiments, the one or more micro-controllers may
directly execute their own embedded logic to perform actions and
access its own internal memory and its own external Input and
Output Interfaces (e.g., hardware pins or wireless transceivers) to
perform actions, such as System On a Chip (SOC), or the like.
Illustrative Network Computer
[0084] FIG. 3 shows one embodiment of network computer 300 that may
be included in a system implementing one or more of the various
embodiments. Network computer 300 may include many more or less
components than those shown in FIG. 3. However, the components
shown are sufficient to disclose an illustrative embodiment for
practicing these innovations. Network computer 300 may represent,
for example, one embodiment of at least one of visualization server
computer 116, data source server computer 118, or the like, of FIG.
1.
[0085] Network computers, such as, network computer 300 may include
a processor 302 that may be in communication with a memory 304 via
a bus 328. In some embodiments, processor 302 may be comprised of
one or more hardware processors, or one or more processor cores. In
some cases, one or more of the one or more processors may be
specialized processors designed to perform one or more specialized
actions, such as, those described herein. Network computer 300 also
includes a power supply 330, network interface 332, audio interface
356, display 350, keyboard 352, input/output interface 338,
processor-readable stationary storage device 334, and
processor-readable removable storage device 336. Power supply 330
provides power to network computer 300.
[0086] Network interface 332 includes circuitry for coupling
network computer 300 to one or more networks, and is constructed
for use with one or more communication protocols and technologies
including, but not limited to, protocols and technologies that
implement any portion of the Open Systems Interconnection model
(OSI model), global system for mobile communication (GSM), code
division multiple access (CDMA), time division multiple access
(TDMA), user datagram protocol (UDP), transmission control
protocol/Internet protocol (TCP/IP), Short Message Service (SMS),
Multimedia Messaging Service (MMS), general packet radio service
(GPRS), WAP, ultra-wide band (UWB), IEEE 802.16 Worldwide
Interoperability for Microwave Access (WiMax), Session Initiation
Protocol/Real-time Transport Protocol (SIP/RTP), or any of a
variety of other wired and wireless communication protocols.
Network interface 332 is sometimes known as a transceiver,
transceiving device, or network interface card (NIC). Network
computer 300 may optionally communicate with a base station (not
shown), or directly with another computer.
[0087] Audio interface 356 is arranged to produce and receive audio
signals such as the sound of a human voice. For example, audio
interface 356 may be coupled to a speaker and microphone (not
shown) to enable telecommunication with others or generate an audio
acknowledgment for some action. A microphone in audio interface 356
can also be used for input to or control of network computer 300,
for example, using voice recognition.
[0088] Display 350 may be a liquid crystal display (LCD), gas
plasma, electronic ink, light emitting diode (LED), Organic LED
(OLED) or any other type of light reflective or light transmissive
display that can be used with a computer. In some embodiments,
display 350 may be a handheld projector or pico projector capable
of projecting an image on a wall or other object.
[0089] Network computer 300 may also comprise input/output
interface 338 for communicating with external devices or computers
not shown in FIG. 3. Input/output interface 338 can utilize one or
more wired or wireless communication technologies, such as USB.TM.,
Firewire.TM., WiFi, WiMax, Thunderbolt.TM., Infrared,
Bluetooth.TM., Zigbee.TM., serial port, parallel port, and the
like.
[0090] Also, input/output interface 338 may also include one or
more sensors for determining geolocation information (e.g., GPS),
monitoring electrical power conditions (e.g., voltage sensors,
current sensors, frequency sensors, and so on), monitoring weather
(e.g., thermostats, barometers, anemometers, humidity detectors,
precipitation scales, or the like), or the like. Sensors may be one
or more hardware sensors that collect or measure data that is
external to network computer 300. Human interface components can be
physically separate from network computer 300, allowing for remote
input or output to network computer 300. For example, information
routed as described here through human interface components such as
display 350 or keyboard 352 can instead be routed through the
network interface 332 to appropriate human interface components
located elsewhere on the network. Human interface components
include any component that allows the computer to take input from,
or send output to, a human user of a computer. Accordingly,
pointing devices such as mice, styluses, track balls, or the like,
may communicate through pointing device interface 358 to receive
user input.
[0091] GPS transceiver 340 can determine the physical coordinates
of network computer 300 on the surface of the Earth, which
typically outputs a location as latitude and longitude values. GPS
transceiver 340 can also employ other geo-positioning mechanisms,
including, but not limited to, triangulation, assisted GPS (AGPS),
Enhanced Observed Time Difference (E-OTD), Cell Identifier (CI),
Service Area Identifier (SAI), Enhanced Timing Advance (ETA), Base
Station Subsystem (BSS), or the like, to further determine the
physical location of network computer 300 on the surface of the
Earth. It is understood that under different conditions, GPS
transceiver 340 can determine a physical location for network
computer 300. In one or more embodiments, however, network computer
300 may, through other components, provide other information that
may be employed to determine a physical location of the client
computer, including for example, a Media Access Control (MAC)
address, IP address, and the like.
[0092] In at least one of the various embodiments, applications,
such as, operating system 306, recommendation engine 322,
visualization engine 324, modeling engine 326, other applications
329, or the like, may be arranged to employ geo-location
information to select one or more localization features, such as,
time zones, languages, currencies, currency formatting, calendar
formatting, or the like. Localization features may be used in user
interfaces, dashboards, visualizations, reports, as well as
internal processes or databases. In at least one of the various
embodiments, geo-location information used for selecting
localization information may be provided by GPS 340. Also, in some
embodiments, geolocation information may include information
provided using one or more geolocation protocols over the networks,
such as, wireless network 108 or network 111.
[0093] Memory 304 may include Random Access Memory (RAM), Read-Only
Memory (ROM), or other types of memory. Memory 304 illustrates an
example of computer-readable storage media (devices) for storage of
information such as computer-readable instructions, data
structures, program modules or other data. Memory 304 stores a
basic input/output system (BIOS) 308 for controlling low-level
operation of network computer 300. The memory also stores an
operating system 306 for controlling the operation of network
computer 300. It will be appreciated that this component may
include a general-purpose operating system such as a version of
UNIX, or Linux.RTM., or a specialized operating system such as
Microsoft Corporation's Windows operating system, or the Apple
Corporation's macOS.RTM. operating system. The operating system may
include, or interface with one or more virtual machine modules,
such as, a Java virtual machine module that enables control of
hardware components or operating system operations via Java
application programs. Likewise, other runtime environments may be
included.
[0094] Memory 304 may further include one or more data storage 310,
which can be utilized by network computer 300 to store, among other
things, applications 320 or other data. For example, data storage
310 may also be employed to store information that describes
various capabilities of network computer 300. The information may
then be provided to another device or computer based on any of a
variety of methods, including being sent as part of a header during
a communication, sent upon request, or the like. Data storage 310
may also be employed to store social networking information
including address books, buddy lists, aliases, user profile
information, or the like. Data storage 310 may further include
program code, data, algorithms, and the like, for use by a
processor, such as processor 302 to execute and perform actions
such as those actions described below. In one embodiment, at least
some of data storage 310 might also be stored on another component
of network computer 300, including, but not limited to,
non-transitory media inside processor-readable removable storage
device 336, processor-readable stationary storage device 334, or
any other computer-readable storage device within network computer
300, or even external to network computer 300. Data storage 310 may
include, for example, data models 314, data sources 316,
visualization models 318, recommendation models 319, or the
like.
[0095] Applications 320 may include computer executable
instructions which, when executed by network computer 300,
transmit, receive, or otherwise process messages (e.g., SMS,
Multimedia Messaging Service (MMS), Instant Message (IM), email, or
other messages), audio, video, and enable telecommunication with
another user of another mobile computer. Other examples of
application programs include calendars, search programs, email
client applications, IM applications, SMS applications, Voice Over
Internet Protocol (VOIP) applications, contact managers, task
managers, transcoders, database programs, word processing programs,
security applications, spreadsheet programs, games, search
programs, and so forth. Applications 320 may include recommendation
engine 322, visualization engine 324, modeling engine 326, other
applications 329, or the like, that may be arranged to perform
actions for embodiments described below. In one or more of the
various embodiments, one or more of the applications may be
implemented as modules or components of another application.
Further, in one or more of the various embodiments, applications
may be implemented as operating system extensions, modules,
plugins, or the like.
[0096] Furthermore, in one or more of the various embodiments,
recommendation engine 322, visualization engine 324, modeling
engine 326, other applications 329, or the like, may be operative
in a cloud-based computing environment. In one or more of the
various embodiments, these applications, and others, that comprise
a visualization platform may be executing within virtual machines
or virtual servers that may be managed in a cloud-based based
computing environment. In one or more of the various embodiments,
in this context the applications may flow from one physical network
computer within the cloud-based environment to another depending on
performance and scaling considerations automatically managed by the
cloud computing environment. Likewise, in one or more of the
various embodiments, virtual machines or virtual servers dedicated
to recommendation engine 322, visualization engine 324, modeling
engine 326, other applications 329, or the like, may be provisioned
and de-commissioned automatically.
[0097] Also, in one or more of the various embodiments,
recommendation engine 322, visualization engine 324, modeling
engine 326, other applications 329, or the like, may be located in
virtual servers running in a cloud-based computing environment
rather than being tied to one or more specific physical network
computers.
[0098] Further, network computer 300 may also comprise hardware
security module (HSM) 360 for providing additional tamper resistant
safeguards for generating, storing or using security/cryptographic
information such as, keys, digital certificates, passwords,
passphrases, two-factor authentication information, or the like. In
some embodiments, hardware security module may be employed to
support one or more standard public key infrastructures (PKI), and
may be employed to generate, manage, or store keys pairs, or the
like. In some embodiments, HSM 360 may be a stand-alone network
computer, in other cases, HSM 360 may be arranged as a hardware
card that may be installed in a network computer.
[0099] Additionally, in one or more embodiments (not shown in the
figures), network computer 300 may include an embedded logic
hardware device instead of a CPU, such as, an Application Specific
Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA),
Programmable Array Logic (PAL), or the like, or combination
thereof. The embedded logic hardware device may directly execute
its embedded logic to perform actions. Also, in one or more
embodiments (not shown in the figures), the network computer may
include one or more hardware microcontrollers instead of a CPU. In
one or more embodiments, the one or more microcontrollers may
directly execute their own embedded logic to perform actions and
access their own internal memory and their own external Input and
Output Interfaces (e.g., hardware pins or wireless transceivers) to
perform actions, such as System On a Chip (SOC), or the like.
Illustrative Logical System Architecture
[0100] FIG. 4 illustrates a logical architecture of system 400 for
content based related view recommendations in accordance with one
or more of the various embodiments. In one or more of the various
embodiments, system 400 may be comprised of various components,
including, one or more modeling engines, such as, modeling engine
402;
[0101] In one or more of the various embodiments, system 400 may be
arranged to include various components including, modeling engine
402, training data store 404, recommendation models data store 406,
recommendation engine 408, data source(s) 410, community
visualizations 412, user visualization 414, visualization engine
416; one or more recommended visualizations 418, or the like.
[0102] In one or more of the various embodiments, recommendation
engines may be arranged to employ one or more recommendation models
to recommend one or more visualizations to users based on one or
more metadata fields associated with visualizations being employed
by a user. Also, in some embodiments, visualization engines may be
arranged to monitor user interactions with the recommended
visualizations to generate metrics or telemetry information that
may be used to evaluate the efficacy of the recommendation models
based on user interactions with the recommended visualizations.
[0103] In one or more of the various embodiments, recommendation
models may be arranged to recommend one or more visualizations
based on the similarly of metadata that may be associated with the
visualizations. In some embodiments, metadata may include text
information that may be considered distinct from the content
included or displayed in the visualizations. For example, in some
embodiments, metadata may include author names, captions,
visualization names, column names, table names, data source names,
author roles, author location, author organization/department, data
model object names, or the like. Note, one of ordinary skill in the
art will appreciate that metadata may vary depending on local
requirements or local circumstances. Thus, examples of metadata
described here or throughout this detailed description should be
considered non-limiting examples of metadata that are at least
sufficient for disclosing the innovations included herein.
[0104] In one or more of the various embodiments, recommendation
models may be arranged to be modular such that various heuristics,
machine learning models, filters, or the like, may be included in a
recommendation model. In one or more of the various embodiments,
modules may be modified, replaced, or added to recommendation
models. Accordingly, in some embodiments, recommendation models may
be tailored to local circumstances or local requirements. Likewise,
in some embodiments, if additional heuristics or machine learning
methods are proven or discovered to be effective in offline
experiments or by observation of production environments, they may
be included in recommendation models.
[0105] In one or more of the various embodiments, one or more
portions of recommendation models may be focused on employing
natural language processing of text content included in the
metadata fields. In some embodiments, different recommendation
models or portions of recommendation models may be arranged to
employ techniques, such as, Latent Semantic Analysis (LSA),
factorization machines (FM), Cosine-similarity, gradient boosting
decision trees (GBDTs), term frequency-inverse document frequency
(TF-IDF), discriminant analysis, or the like, for content based
related view recommendations. In some embodiments, different types
or instances of recommendation models may be trained or tested for
different circumstances. For some embodiments, one of ordinary
skill in the art will appreciate the recommendation models may be
subject to continuous testing, tuning, or improvement while in use
by an organization. In some cases, existing recommendation models
may fall out of favor while new recommendation models, perhaps
comprising new modeling methods, heuristics, parameters, or the
like, may be deployed. Thus, one of ordinary skill in the art will
appreciate that these innovations are not limited to fixed set of
recommendation models rather they may be expected to change or
evolve overtime within a given organization or access
organizations.
[0106] In one or more of the various embodiments, because
recommendation models may be arranged to support individual
organizations or users of individual organizations, each
organization may have different training data. Accordingly, in some
embodiments, training data may vary widely in quantity or
characteristics. In some cases, organizations may have accumulated
many visualizations before content based related view
recommendations has been enabled. In contrast, in some embodiments,
other organizations may have activated visualization
recommendations before they have created enough visualizations for
training their recommendation models.
[0107] Accordingly, in some embodiments, one or more baseline
models may be provided to enable meaningful recommendations for
organizations that may not have sufficient training data. In some
embodiments, baseline models may be trained based on training data
that may be based on public shared data. In some embodiments,
shared data may be provided by other users or organizations that
have volunteered to provide some or all of their visualizations for
training baseline models.
[0108] Thus, in some embodiments, organizations that lack
sufficient data for training their own recommendation models may be
provided one or more recommendation models that are based on
baseline models. As they generate their own training data, it may
be employed for training recommendation models based on their own
data.
[0109] FIG. 5 illustrates a logical schematic of a portion of
system 200 showing some metadata for content based related view
recommendations in accordance with one or more of the various
embodiments. In some embodiments, visualization systems may be
arranged to define various metadata for visualizations. Also, in
one or more of the various embodiments, visualization systems may
be arranged to define metadata for other parts of the visualization
system, such as, users, data sources, projects, annotations, tags
associated with objects in the system, permissions/privileges
associated with objects or users, or the like. In some embodiments,
one or more metadata fields may be included or omitted from
consideration depending on local circumstances or local
requirements.
[0110] Accordingly, in some embodiments, metadata fields determined
to be relevant or otherwise useful for content based related view
recommendations may be determined based on configuration
information.
[0111] In one or more of the various embodiments, metadata may be
associated with different scopes or contexts. In this example,
table 502 represents a list of table names that may be defined for
a database schema or the like.
[0112] Also, in this example, for some embodiments, table 504
represents a list of metadata fields alongside their data type. In
this example, table 504 may be considered to include the columns
defined for a table that stores metadata associated with
visualizations.
[0113] Finally, in this example, table 506 represent a table with
metadata field values for visualizations, such that each row
represents metadata field values for a separate visualization.
[0114] One of ordinary skill in the art will appreciate that in
production environments there may be more or different metadata
fields than depicted here. However, one of ordinary skill in the
art will appreciate the metadata depicted here is sufficient for at
least disclosing the innovations described herein. Similar, while
this example represents metadata using tables, the one or more data
structures employed to represent metadata fields or collections of
metadata fields may use a variety of data structures, including,
lists, arrays, dictionaries, vectors, sparse arrays, trees, or the
like, without departing from the scope of these innovations.
[0115] FIG. 6 illustrates a logical representation of a portion of
user interface 600 for content based related view recommendations
in accordance with one or more of the various embodiments. In some
embodiments, user interface 600 may be arranged to include one or
more panels, such as, panel 602, panel 604, or the like.
[0116] In one or more of the various embodiments, user interface
600 may be arranged to display one or more recommended
visualizations to a user. In some embodiments, panels, such as,
panel 604 may display compact representations (e.g., thumbnails, or
the like) of recommended visualizations. In this example, sub-panel
606 may represent a compact visual representation of a recommended
visualization. Also, in some embodiments, additional information,
including explanatory natural language explanation narratives may
be displayed (or accessed via) sub-panels, such as, sub-panel 608.
For example, in some embodiments, sub-panel 608 may include a
narrative that explains which metadata fields had the most
influence on in recommending the visualization shown in sub-panel
606.
[0117] In one or more of the various embodiments, the panels
associated with the recommended visualizations may be displayed in
rank order based on a recommendation score associated with the
quality or strength of a given recommendation.
[0118] Accordingly, in some embodiments, users may be enabled to
select a visualization from the collection of displayed recommended
visualizations.
[0119] In one or more of the various embodiments, panels, such as,
panel 604 may be associated with other User Interface elements that
may enable users to provide a score that represents their agreement
or disagreement with the recommendations. Accordingly, in some
embodiments, modeling engines may employ these scores to modify or
grade recommendation models for the user or other users in the same
organization.
[0120] In one or more of the various embodiments, explanatory
narratives associated with the recommended visualizations may
describe the reason why a given visualization has been recommended.
In some embodiments, narrative text may be associated with one or
more metadata fields. For example, for some embodiments, if the
strongest signal for recommending a visualization may be the
content of its caption field, the narrative information associated
with the recommendation may include narrative text that explains
that recommendation has been made based on the similarity of the
caption to the captions of the other visualizations used by the
user.
[0121] In one or more of the various embodiments, user interface
600 may be arranged to enable users to drill down (e.g., through
sub-panel 608) to explore additional details associated with a
given recommendation.
Generalized Operations
[0122] FIGS. 7-13 represent generalized operations for content
based related view recommendations in accordance with one or more
of the various embodiments. In one or more of the various
embodiments, processes 700, 800, 900, 1000, 1100, 1200, and 1300
described in conjunction with FIGS. 7-13 may be implemented by or
executed by one or more processors on a single network computer (or
network monitoring computer), such as network computer 300 of FIG.
3. In other embodiments, these processes, or portions thereof, may
be implemented by or executed on a plurality of network computers,
such as network computer 300 of FIG. 3. In yet other embodiments,
these processes, or portions thereof, may be implemented by or
executed on one or more virtualized computers, such as, those in a
cloud-based environment. However, embodiments are not so limited
and various combinations of network computers, client computers, or
the like may be utilized. Further, in one or more of the various
embodiments, the processes described in conjunction with FIGS. 7-11
may be used for content based related view recommendations in
accordance with at least one of the various embodiments or
architectures such as those described in conjunction with FIGS.
4-6. Further, in one or more of the various embodiments, some or
all of the actions performed by processes 700, 800, 900, 1000,
1100, 1200, and 1300 may be executed in part by recommendation
engine 322, visualization engine 324, modeling engine 326 one or
more processors of one or more network computers.
[0123] FIG. 7 illustrates an overview flowchart for process 700 for
content based related view recommendations in accordance with one
or more of the various embodiments. After a start block, at block
702, in one or more of the various embodiments, a visualization
associated with a collection of community visualizations may be
displayed. Typically, this visualization may a visualization that a
user may be interacting with via a user interface or interactive
report. In some embodiments, if a user may be viewing more than one
visualization, recommendations may be generated for one or more the
display visualizations.
[0124] At block 704, in one or more of the various embodiments,
recommendation engines may be arranged to determine one or more
metadata fields associated with displayed visualization. At block
706, in one or more of the various embodiments, the recommendation
engines may be arranged to rank the one or more community
visualizations based on the similarities of the metadata fields
associated with each community visualization with the metadata
fields associated with the displayed visualization.
[0125] In one or more of the various embodiments, recommendation
engines may be arranged to employ recommendation models to generate
recommendation scores for each community visualizations. In one or
more of the various embodiments, recommendation engines may be
arranged to rank the community visualizations based on the
recommendation scores associated with each community
visualization.
[0126] At block 708, in one or more of the various embodiments,
recommendation engines may be arranged to determine influence
scores for each metadata field of the recommended visualizations
based on their contribution to the recommendation scores.
[0127] At block 710, in one or more of the various embodiments,
recommendation engines may be arranged to provide information that
may be provided in a user interface or interactive report showing
the one or more recommended visualizations to a user. Next, in one
or more of the various embodiments, control may be returned to a
calling process.
[0128] FIG. 8 illustrates a flowchart for process 800 for
generating recommendation models for content based related view
recommendations in accordance with one or more of the various
embodiments. After a start block, at block 802, in one or more of
the various embodiments, modeling engines may be arranged to
determine one or more metadata fields in one or more community
visualizations. In one or more of the various embodiments, one or
more metadata fields may be determined unsuitable for using for
content based related view recommendations.
[0129] Accordingly, in some embodiments, recommendation engines may
be arranged to selectively determine metadata field for
consideration from among a larger set of metadata fields. In some
embodiments, recommendation engines may be arranged to employ
rules, lists, filters, or the like, provided via configuration
information to determine the metadata fields considers providing
recommendations.
[0130] Also, in one or more of the various embodiments, one or more
recommendation models may be arranged to provide an interface that
defines the metadata fields that may be required (or optional) for
the recommendation models to generate recommendation scores for
recommended visualizations.
[0131] At block 804, in one or more of the various embodiments, the
modeling engines may be arranged to prepare the metadata using one
or more heuristic methods to modify or filter the one or more
metadata fields. In one or more of the various embodiments, one or
more conventional data preparation methods may be applied to values
of one or more metadata fields, such as, tokenizing, removing stop
words, normalizing capitalization, spelling correction, or the
like. In some embodiments, semantic analysis may be employed to
collapse one or more metadata field values or otherwise identify
differing content that may have the same meaning.
[0132] In one or more of the various embodiments, one or more
metadata fields may be associated with particular heuristics of
filters. For example, metadata fields that may be restricted to
single word values may be processed differently than metadata
fields that often include sentences or paragraphs of words.
[0133] At block 806, in one or more of the various embodiments, the
modeling engines may be arranged to train one or more
recommendation models based on the prepared metadata. In one or
more of the various embodiments, recommendation models may be
arranged to include one or more sub-models or modules that require
training. Accordingly, in some embodiments, recommendation engines
may be arranged to execute the required training as defined for a
given recommendation model.
[0134] At block 808, in one or more of the various embodiments,
recommendation engines may be arranged to employ the one or more
recommendation models to recommend one or more visualizations based
on similarity of metadata. Next, in one or more of the various
embodiments, control may be returned to a calling process.
[0135] FIG. 9 illustrates a flowchart for process 900 for content
based related view recommendations in accordance with one or more
of the various embodiments. After a start block, at block 902, in
one or more of the various embodiments, recommendation engines may
be arranged to determine one or more metadata fields associated
with the input visualization. In some embodiments, input
visualizations may be visualizations currently being displayed to a
user.
[0136] In one or more of the various embodiments, input
visualizations may be selected based on the current visualizations
a user may be interacting with. In some embodiments, recommendation
engines may be arranged to automatically employ visualizations that
users may be actively viewing as input visualizations. In some
embodiments, user interfaces may include one or more user interface
controls that enable a user to activate recommendation features or
selectively request to receive recommendation reports.
[0137] In one or more of the various embodiments, recommendation
engines may be arranged to iterate across various metadata sources
to determine the metadata fields that may be employed as input
values for determining one or more recommended visualizations. As
described above, in some embodiments, recommendation engines may be
arranged to determine metadata fields from one or more of data
sources, database tables, visualization models, data models, or the
like. Also, in one or more of the various embodiments,
recommendation engines may be arranged to determine one or more
metadata fields from telemetry information, such as, view counts,
last-used dates, other popularity metrics, or the like.
[0138] In one or more of the various embodiments, recommendation
engines may be arranged to filter, format, merge, modify, or the
like, metadata fields or metadata fields values to conform to input
specifications of one or more recommendation models. For example,
in some cases, a metadata field such as `table_name` may be
concatenated with a column name such as `last_accessed` to provide
metadata field `table_name.last_accessed` to distinguish it from
other similarly named columns.
[0139] In some embodiments, recommendation engines may be arranged
to store one or more metadata fields using fully qualified
identifiers that may distinguish them from other similarly named
metadata fields.
[0140] Also, in one or more of the various embodiments,
recommendation engines may be arranged to perform one or more
actions to clean, normalize, or other prepare metadata field values
before providing them to recommendation models. In some
embodiments, this may include correcting spelling, normalization
capitalization, rounding or truncating numerical values, word
substitution (e.g., mapping semantically similar words a common
word), truncating long strings, removing so-called stop words from
phrases, or the like.
[0141] Also, in one or more of the various embodiments,
recommendation engines may be arranged to perform additional
actions to pre-process or pre-compute one or more attributes,
statistics, metrics, or the like, associated with metadata fields
or metadata fields values. For example, in some embodiments,
recommendation engines may be arranged to compute one or more
metrics, such as, entropy, perplexity, or the like, for (natural
language) text phrases or text block included in one or more
metadata fields. In some embodiments, recommendation engines may be
arranged to compute or generate one or more synthetic metadata
fields that may be included with the one or more metadata fields
pulled from other sources.
[0142] Accordingly, in some embodiments, recommendation engines may
be arranged to determine rules, locations, or the like, for
determining metadata fields sources, metadata fields, metadata
field formatting, or the like based on configuration information to
account for local circumstances or local requirements.
[0143] At block 904, in some embodiments, recommendation engines
may be arranged to determine one or more community visualizations
to consider for recommendation. In one or more of the various
embodiments, community visualizations available to a user may
include one or more community visualizations that may be excluded
from consideration for various reasons. In some embodiments, one or
more community visualizations may be excluded based on user
preferences, organization preferences, or the like. Also, in some
embodiments, recommendation models may include one or more
front-side heuristics that exclude one or more community
visualizations from consideration. For example, in some
embodiments, a user may prefer to exclude community visualizations
that may be associated with certain business units, or the like.
Also, for example, for some embodiments, users may be enabled to
exclude visualizations for various reasons such as, various
metadata field values (e.g., age, last-accessed-date, user, or the
like), languages (e.g., English, Spanish, or the like), types of
visualizations, or the like.
[0144] Further, in one or more of the various embodiments,
recommendation engines may be arranged to enable users or
organizations to exclude one or more visualizations from being
considered for recommendation based on one or more metadata field
values that experience indicates may be associated with
visualizations that may be unsuitable consideration for
recommendations. For example, in some embodiments, users may
observe that visualizations associated with particular tag/status
metadata field values such as, "test," "broken," "private,"
"incomplete," or the like, should be excluded from consideration
for recommendations.
[0145] Likewise, in some embodiments, modeling engines may be
arranged to identify one or more metadata field values that
indicate certain visualization may be unsuitable consideration for
recommendations. For example, in some embodiments, during
recommendation model training, visualizations associated with
tag/status metadata field values such as, "test," "broken,"
"private," "incomplete," or the like, may be determined to be
excluded from consideration.
[0146] Further, in some embodiments, one or more particular
recommendation models may be ineffective at providing
recommendations for one or more classes or categories of
visualizations while at the same time being effective for other
classes or categories of visualizations. Thus, in some embodiments,
one or more community visualizations may be excluded from
consideration depending on the recommendation models being
used.
[0147] At block 906, in one or more of the various embodiments,
recommendation engines may be arranged to employ one or more
content focused classifiers, or the like, to determine one or more
partial recommendation scores for one or more community
visualizations based on metadata field similarity with the input
visualization.
[0148] In one or more of the various embodiments, modeling engines
may be arranged to generate recommendation models that include one
or more portions or one or more sub-models that may be trained or
otherwise directed to employ content or values of metadata fields
to generate recommendation scores associated with the similarity of
metadata field content with the community visualizations that may
be available.
[0149] Accordingly, in one or more of the various embodiments,
recommendation engines may be arranged to compare metadata field
values from the input visualization with metadata field values from
one or more community visualizations.
[0150] In one or more of the various embodiments, one or more
sub-models comprising a recommendation model may be directed to
evaluating or comparing one or more metadata fields that include
text values. Also, in some embodiments, one or more sub-models
comprising recommendation models may be directed to evaluating
metadata fields that include numerical values.
[0151] In one or more of the various embodiments, one or more
community visualizations may be associated with one or more
aberrations, such as, one or more missing/omitted metadata fields,
one or more empty metadata fields, or the like. Accordingly, in
some embodiments, one or more recommendation models may be arranged
to provide default values as needed. Alternatively, in some
embodiments, recommendation engines or recommendation models may be
arranged to exclude one or more community visualizations from
consideration based on one or more missing metadata field
values.
[0152] At block 908, in one or more of the various embodiments,
recommendation engines may be arranged to generate one or more
other partial recommendation scores based on one or more of
recency, popularity, local preferences, rules, or the like.
[0153] In one or more of the various embodiments, recommendation
models may be arranged to include one or more sub-models that are
based on heuristics or otherwise may not be determined based on
machine learning training, or the like. In some embodiments, these
may include sanity checks that may be directed at de-ranking one or
more visualizations that may be outliers or otherwise demonstrably
anomalous. For example, in some embodiments, visualizations older
than a defined age threshold may be de-ranked. Further, for
example, visualizations that omit one or more metadata fields may
be de-ranked to account for the impact of assigned default values
that may be associated with strong signal metadata fields, or the
like.
[0154] In one or more of the various embodiments, recommendation
models may be arranged to define one or more conditions, rules,
threshold values, or the like, for generating partial scores based
on one or more metrics associated with the community
visualizations. In some embodiments, one or more of these metrics
may depend on telemetry information associated with other users
that may be collected by visualization engines, such as, number of
views by other users, number times a visualization is recommended
to other users, number of times a visualization is selected from a
recommendation list by other users, or the like. Likewise, in some
embodiments, similar telemetry metrics for the current user may be
employed in rules, conditions, or the like, for scoring community
visualizations.
[0155] At block 910, in one or more of the various embodiments, the
recommendation engines may be arranged to generate one or more
recommendation scores based on combinations of the one or more
partial recommendation score, the one or more other partial
recommendation scores, or the like.
[0156] In one or more of the various embodiments, recommendation
models may be arranged to include one or more formulas for
computing recommendation scores from one or more partial scores. In
some embodiments, recommendation models may be arranged to compute
recommendation scores based on linear combination of the partial
scores. In some embodiments, a sum of some partial scores may be
divided a sum of other scores, or the like. Accordingly, in some
embodiments, the particular combination formula may be defined as
part of a recommendation model.
[0157] In one or more of the various embodiments, if the partial
scores may be based on more than recommendation model, individual
recommendation models may be associated with weights or
coefficients that influence their contribution to the overall
recommendation scores associated with each community
visualization.
[0158] Further, in some embodiments, recommendation engines may be
arranged to consider additional scores, such as, confidence scores,
or the like, that may be associated one or more partial scores if
partial scores are being combined.
[0159] At block 912, in one or more of the various embodiments, the
recommendation engines may be arranged to rank order one or more
community visualizations based on the recommendation scores. In one
or more of the various embodiments, recommendation engines may be
arranged to employ the recommendation scores associated with each
community visualization that was evaluated to rank the one or more
community visualizations.
[0160] At block 914, in one or more of the various embodiments,
recommendation engines may be arranged to filter the rank ordered
list of recommended visualizations. In one or more of the various
embodiments, recommendation engines may be arranged to apply one or
more filters to reduce the number of community visualizations in
the rank ordered list. Alternatively, in some embodiments,
recommendation engines may be arranged to provide all of the rank
ordered visualizations to a visualization engine that may determine
how many to display in a user interface.
[0161] Also, in one or more of the various embodiments,
recommendation models may be arranged to include one or more
filters that may be executed on the collection of rank ordered
community visualizations. In one or more of the various
embodiments, these back-side filters may be employed to modify the
ranking or discard one or more community visualizations from the
collection. In one or more of the various embodiments, a
recommendation model may be arranged to demote a ranked
visualization because its position may appear anomalous or
spurious. For example, for some embodiments, if a visualization may
be ranked higher than other visualizations because it had extremely
high partial scores associated with one or more metadata fields
that historically may be poorly correlated with user preferences,
that visualization may be bumped down in rank to be more consistent
with its partial scores associated with more influential metadata
fields.
[0162] In one or more of the various embodiments, recommendation
engines may be arranged to store information that includes the
recommendations/ranking along with other relevant information, such
as, the recommendation models that were used, the user, the
organization, time/date the recommendations were made, or the like.
Accordingly, in some embodiments, the efficacy of recommendations
or recommendation models may be evaluated later.
[0163] Next, in one or more of the various embodiments, control may
be returned to a calling process.
[0164] FIG. 10 illustrates a flowchart for process 1000 for content
based related view recommendations in accordance with one or more
of the various embodiments. After a start block, at block 1102, in
one or more of the various embodiments, recommendation engines may
be arranged to determine one or more top ranked recommended
visualizations based on recommendation models or recommendation
scores.
[0165] Also, in some embodiments, recommendation engines may be
arranged to evaluate the contribution individual metadata fields to
the determination of the recommendation score for each recommended
visualization.
[0166] Accordingly, in some embodiments, recommendation models may
include one or more modules that may be directed to determining
influence scores for one or more of the metadata fields used for
determining the one or more recommended visualizations.
[0167] At block 1004, in one or more of the various embodiments,
recommendation engines may be arranged to determine one or more
influence scores for one or more of the metadata fields associated
with each of the top ranked recommended visualizations.
[0168] In one or more of the various embodiments, recommendation
engines may be arranged to employ one or more recommendation models
that may define different strategies, criteria, or the like, for
identifying metadata field contributions to recommendation models
associated with visualizations. However, in some embodiments, the
relative contribution of a metadata field may be represented by one
or more values referred to herein as influence scores.
[0169] Accordingly, in one or more of the various embodiments,
below listed examples may be based on instructions, rules,
sub-models, or the like, included in recommendation models. For
clarity or brevity portions of the recommendation models associated
with determining influence scores of metadata fields may be
referred to as influence models.
[0170] In one or more of the various embodiments, one or more
influence models may be arranged to rank metadata fields based on
the evaluating commonalities of the values for a given metadata
field across the ranked visualizations. Accordingly, in some
embodiments, influence scores (or partial scores) of metadata
fields may be based on a count of the number top ranked
visualizations that share the same values. In some embodiments,
recommendation engines may be arranged to one or more comparisons
or evaluation to determine if metadata fields for visualizations
have a common value. In some embodiments, metadata field values in
visualizations may be tokenized before comparing with values from
other visualizations. Also, in one or more of the various
embodiments, metadata field values in visualizations may be
compared directly with values from other visualizations. Further,
in some embodiments, one or more fuzzy match strategies may be
employed to compare metadata field values across
visualizations.
[0171] In some embodiments, one or more influence models may be
arranged determine influence scores (or partial scores) by
computing the effects on the recommendation scores if individual
metadata fields are removed from consideration. Accordingly, in one
or more of the various embodiments, the impact of individual
metadata fields may be evaluated based on the how much
recommendation scores or rank order of visualizations change if a
given metadata field is omitted during the determination of
recommendation scores. Thus, in some embodiments, the changes to
recommendation scores associated with individually omitted metadata
fields may be measured or otherwise mapped to influence scores or
partial influence scores.
[0172] In one or more of the various embodiments, recommendation
engines may be arranged to train one or more train one or more
topic models based on Latent Semantic Analysis (LSA), or the like,
to determine dominant topics for ranked visualizations based on the
metadata fields. Accordingly, in some embodiments, values included
in metadata fields may be evaluated using topic models to identify
the dominant topics. Thus, in some embodiments, the dominant topics
may be mapped back to their associated metadata fields to evaluate
the influence of each metadata field.
[0173] In some embodiments, one or more metadata fields may be
excluded from one or more evaluations based on various
considerations, such as, privacy, efficacy, relevancy, empirical
evidence of providing misleading signals, or the like. In some
embodiments, recommendation engines may be arranged to incorporate
user feedback or telemetry information to evaluate the efficacy or
quality of the influence scores. In some embodiments, metadata
fields that may be associated with misleading results may be down
weighted or removed from consideration.
[0174] Also, in some embodiments, recommendation models may be
arranged to weight partial influence scores provided by influence
models based on performance, user preference, telemetry
information, or the like. Further, in some embodiments, different
influence models may be selected based on one or more
considerations, such as, number of metadata fields, distribution of
recommendation scores, or the like. Further, in some embodiments,
influence models may be applied progressively such that the results
of a prior executed influence model may influence the selection of
a subsequent influence model. For example, for some embodiments, if
a first influence model provides result with a confidence value
that exceeds a threshold value, the determination of influence
score may be halted. In contrast, for some embodiments, if a first
influence models provides inconclusive results, another influence
model may be selected and executed.
[0175] In one or more of the various embodiments, recommendation
engines may be arranged to combine two or more partial influence
scores associated with different influence models to provide a
single influence score for one or more metadata fields. In one or
more of the various embodiments, recommendation models may include
rules, formulas, instructions, or the like, for combining partial
influence scores into a single score that may be associated with a
metadata field.
[0176] At block 1006, in one or more of the various embodiments,
the recommendation engines may be arranged to rank the one or more
metadata fields for each top ranked recommended visualization based
on the one or more influence scores.
[0177] In some embodiments, recommendation engines may be arranged
to provide an overall ranking of metadata fields as well as ranking
metadata fields within each visualization. For example, in some
embodiments, the ranking/score of each metadata field across the
visualizations may be considered to determine an overall ranking of
a given metadata field.
[0178] At block 1008, in one or more of the various embodiments,
the recommendation engines may be arranged to generate narratives
for one or more top ranked recommended visualizations.
[0179] In one or more of the various embodiments, recommendation
models may include modules or sub-models directed to mapping
explanatory text narratives to top ranked metadata fields for
display to users. In some embodiments, one or more templates may be
pre-generated for one or more different metadata fields.
Accordingly, in some embodiments, one or more metadata fields may
be associated with a narrative that may provide a human readable
description of the metadata field or its influence on the
recommendations.
[0180] At block 1010, in one or more of the various embodiments,
the recommendation engines may be arranged to include the
narratives for the one or more top ranked metadata fields in report
information associated with the one or more top ranked recommended
visualizations.
[0181] In one or more of the various embodiments, recommendation
engines may be arranged to provide the ranked list of recommended
visualizations to a visualization engine for display to users in a
user interface or interactive report.
[0182] Next, in one or more of the various embodiments, control may
be returned to a calling process.
[0183] FIG. 11 illustrates a flowchart for process 1100 for
providing initial recommendation models based on a baseline model
in accordance with one or more of the various embodiments. After a
start block, at block 1102, in one or more of the various
embodiments, recommendation engines may be arranged to provide one
or more baseline models that may be based on public or common
training data.
[0184] In one or more of the various embodiments, if an
organization begins using the visualization system, customized or
personalized recommendation models may be unavailable. Accordingly,
one or more baseline models that may be based on public, shared, or
common data may be provided. In some embodiments, baseline models
may be trained using community data. In some embodiments, community
data may be based on real data associated with other organizations.
Accordingly, in some embodiments, community data may be real data
that has been stripped of sensitive information. In some
embodiments, organizations may be enabled to opt-in to have some or
all of their historical interaction information included in a
community data program.
[0185] Also, in some embodiments, a visualization system may offer
a free or public service that may be used for collecting
interaction information that may employed to train baseline
models.
[0186] In one or more of the various embodiments, baseline models
may be directly derived from one or more existing recommendation
models. In some embodiments, less complex or less detailed versions
of recommendation models may be used as baseline models. In some
cases, for some embodiments, recommendation models expressly
trained for one or more organizations may be used as baseline
models for new organizations.
[0187] In one or more of the various embodiments, different
baseline models may be provided for use with different types of
organizations or users. In some embodiments, baseline models may be
maintained for different types of industries, problem domains,
countries, or the like. Accordingly, in one or more of the various
embodiments, commonalities that may exist within industries,
problem domains, countries, or the like, may be represented in one
or more baseline models.
[0188] At block 1104, in one or more of the various embodiments,
recommendation engines may be arranged to generate one or more
recommendation models based on the one or more baseline models and
one or more characteristics of the organization.
[0189] In one or more of the various embodiments, an initial
organization profile may be developed based on known or provided
information. In one or more of the various embodiments, an
organization profile may include information, such as, industry,
problem domain, country, number of employees, enterprise size,
revenue, or the like.
[0190] Accordingly, in some embodiments, a recommendation engine
may be arranged to map one or more baseline models to an
organization based on one or more mapping rules. In some
embodiments, recommendation engines may be arranged to determine
the rules for mapping baseline models to organizations based on
configuration information.
[0191] At block 1106, in one or more of the various embodiments,
recommendation engines may be arranged to recommend one or more
visualizations to users based on metadata field similarity. In one
or more of the various embodiments, recommendation engines may be
arranged to employ metadata fields of a displayed visualization as
inputs to one or more recommendation models to generate
recommendation scores for one or more community visualizations.
Accordingly, in some embodiments, one or more visualizations may be
recommended based on the recommendation scores generated by the one
or more recommendation models.
[0192] At block 1108, in one or more of the various embodiments,
recommendation engines may be arranged to collect user telemetry or
user feedback associated with the recommended visualizations. In
one or more of the various embodiments, if recommended
visualizations are presented to user, the recommendation engines
may request feedback from the users regarding agreement or
disagreement with the recommendations or the ranking of the
recommended visualizations.
[0193] Also, in one or more of the various embodiments,
visualization engines may be arranged to monitor how users interact
with recommended visualizations to provide metrics that may be used
for evaluating the effectiveness of the recommendation models that
determined the recommended visualizations.
[0194] At block 1110, in one or more of the various embodiments,
recommendation engines may be arranged to progressively update one
or more recommendation models based on changes to organization
information, user feedback, user telemetry, number of available
community visualizations, or the like. In one or more of the
various embodiments, if one or more metrics associated with the
information collected for an organization or the users may exceed
one or more thresholds, modeling engines may be arranged to
re-train or discard one or more associated recommendation models.
Also, in some embodiments, the collected metrics or information may
be stored for inclusion in training data for an organization.
[0195] Next, in one or more of the various embodiments, control may
be returned to a calling process.
[0196] FIG. 12 illustrates a flowchart for process 1200 for
evaluating metadata fields to provide a summary narrative or
explanation associated the recommended visualizations in accordance
with one or more of the various embodiments. After a start block,
at block 1202, in one or more of the various embodiments, as
described above, recommendation engines may be arranged to
determine one or more recommended visualizations. In one or more of
the various embodiments, the recommendations may be based on a
visualization a user may be interacting with.
[0197] According, in some embodiments, recommendation engines may
be arranged to evaluate metadata fields associated with the
recommended visualizations to determine which metadata fields had
the most influence on determining the recommendations.
[0198] At block 1204, in one or more of the various embodiments,
optionally, recommendation engines may be arranged to generate
meta-attributes based on the metadata fields associated with anchor
visualization and the one or more recommended visualizations.
[0199] In one or more of the various embodiments, meta-attributes
may be generated by combining metadata field labels with their
respective metadata field values. For example, in some embodiments,
if a visualization has a metadata field of `tablename` with a value
of `sales`, the meta-attribute `tablename sales` may be generated.
Likewise, for example, a metadata field of `fieldname` with a value
`campaign name` may produce a meta-attribute of `fieldname_campaign
name`.
[0200] Alternatively, in some embodiments, meta-attributes may be
represented using tuples, such as, referring to the two examples
above, (`tablename`, `sales`) or (`fieldname`, `campaign name`), or
the like. Similarly, one of ordinary skill in the art will
appreciate that other data structures or formats may be used to
represent meta-attributes without departing from the scope of these
innovations.
[0201] In some embodiments, the portion of the meta-attribute that
corresponds to the metadata field value, may be considered a
meta-attribute value or the simply the metadata field value.
[0202] Note, this block is marked optional because in some
embodiments, meta-attributes may be generated prior to the
execution of process 1200. For example, recommendation engines may
be arranged to generate meta-attributes when metadata field
information associated with the community visualizations is
processed.
[0203] At block 1206, in one or more of the various embodiments,
recommendation engines may be arranged to determine one or more
meta-attributes that may be common to an anchor visualization and
the one or more recommended visualizations. In some embodiments,
common meta-attributes may be meta-attributes that are same across
all of the recommended visualizations and the anchor
visualization.
[0204] In some embodiments, recommendation engines may be arranged
to employ different "sameness" rules, such as, equality, pattern
matching, fuzzy matching, or the like for determine the common
meta-attributes. Accordingly, in some embodiments, the
recommendation model or other configuration information may include
the rules or instruction for determining sameness for a given
metadata field.
[0205] At block 1208, in one or more of the various embodiments,
recommendation engines may be arranged to tokenize the one or more
common meta-attributes values (or metadata field values associated
with the common meta-attributes) and determine the frequency of
each token.
[0206] For example, in some embodiments, if `fieldname region`,
`tablename campaign`, fieldname_profit`, fieldname_campaign name`,
datasource_marketing`, or the like, are determined to be common
meta-attributes, tokenizing them may produce tokens, such as,
region, campaign, profit, campaign, name, marketing, or the
like.
[0207] In some embodiments, if the tokens may be generated,
recommendation engines may be arranged to determine the frequency
of occurrence of each token. In the example above, frequency
determination would provide (region, 1), (campaign, 2), (name, 1),
(marketing, 1), or the like, with the number of occurrences of each
token associated with the given token. Note, one of ordinary skill
in the art will appreciate that recommendation engines may employ
other data structures or data representations to represent token
frequency information rather than being limited to using pairs or
tuples.
[0208] At block 1210, in one or more of the various embodiments,
recommendation engines may be arranged to perform a
permission/sensitivity evaluation of the tokens. In one or more of
the various embodiments, recommendation engines may be arranged to
restrict users from viewing one or more metadata fields depending
on user access privileges. Thus, in some embodiments, some users
may be restricted from viewing one or more meta-attributes,
metadata fields, tokens, or the like. Accordingly, in one or more
of the various embodiments, recommendation engines may be arranged
to filter one or more of metadata fields, meta-attributes, tokens,
or the like, depending on configuration information or the
recommendation model.
[0209] At block 1212, in one or more of the various embodiments,
recommendation engines may be arranged to rank order the one or
more tokens based on the frequency of their occurrence. In one or
more of the various embodiments, recommendation engines may be
arranged to sort the remaining (after filtering for
permission/privacy) tokens based their frequency of occurrence.
Accordingly, in some embodiments, in this context, frequency of
occurrence may be considered an influence score for the metadata
fields.
[0210] At block 1214, in one or more of the various embodiments,
recommendation engines may be arranged to the top ranked tokens to
a user interface for display to a user. In one or more of the
various embodiments, these top ranked tokens may be provided to
users to provide them an explanation for why the recommended
visualizations were selected. For example, if the top ranked tokens
are Sales, Profits, and Revenue, an explanation narrative such as
"These visualizations are recommended to you based on Sales,
Profits, or Revenue."
[0211] Next, in one or more of the various embodiments, control may
be returned to a calling process.
[0212] FIG. 13 illustrates a flowchart for process 1300 for
evaluating metadata field influence between selected visualizations
in accordance with one or more of the various embodiments. After a
start block, at block 1302, in one or more of the various
embodiments, a user may be enabled to select two or more
visualizations. In some embodiments, user may select the two or
more visualizations via a user interface that may be displaying or
referencing a plurality of visualizations. In some embodiments, the
two or more visualizations may include an anchor visualization, or
one or more recommended visualizations.
[0213] At block 1304, in one or more of the various embodiments,
optionally, recommendation engines may be arranged to generate
meta-attributes based on the metadata fields associated with the
anchor visualization and the one or more recommended
visualizations.
[0214] In one or more of the various embodiments, meta-attributes
may be generated similar as described for block 1204 in process
1200.
[0215] Note, this block is marked optional because in some
embodiments, meta-attributes may be generated prior to the
execution of process 1300. For example, recommendation engines may
be arranged to generate meta-attributes when metadata field
information associated with the community visualizations is
processed.
[0216] At block 1306, in one or more of the various embodiments,
recommendation engines may be arranged to determine the common
meta-attributes in the two or more selected visualizations. In some
embodiments, common meta-attributes may be meta-attributes that are
same across all of the two or more selected visualizations.
[0217] In some embodiments, recommendation engines may be arranged
to employ different "sameness" rules, such as, equality, pattern
matching, fuzzy matching, or the like for determine the common
meta-attributes. Accordingly, in some embodiments, the
recommendation model or other configuration information may include
the rules or instruction for determining sameness for a given
metadata field.
[0218] At block 1308, in one or more of the various embodiments,
recommendation engines may be arranged to determine one or more
weight factors for the common meta-attributes based on the
occurrences of the meta-attributes in the two or more selected
visualizations. In one or more of the various embodiments,
recommendation engines may be arranged to determine the occurrence
count for each common meta-attribute.
[0219] At block 1310, in one or more of the various embodiments,
recommendation engines may be arranged to generate an influence
score for metadata fields based on the product of the
meta-attributes weight factors.
[0220] For example, if the meta-attribute occurrence counts for the
anchor visualization are (fieldname_a, 2), (fieldname_b, 2),
(fieldname_c, 1), and (fieldname_d, 1); and the meta-attribute
occurrence counts for the selected visualization are (fieldname_a,
1), fieldname_b, 1), (fieldname_c, 3), and (fieldname_d, 1); the
meta-attributes influence scores may be (fieldname_c, 1*3=3),
(fieldname_a, 2*1=2), (fieldname_b, 2*1=2), and (fieldname_d,
1*1=1).
[0221] At block 1312, in one or more of the various embodiments,
recommendation engines may be arranged to rank the metadata fields
based on their corresponding metadata fields.
[0222] At block 1314, in one or more of the various embodiments,
recommendation engines may be arranged to provide the rank ordered
metadata fields to a user interface for display to the user. In one
or more of the various embodiments, the top ranked metadata fields
may be employed in an explanation why the two or more
visualizations were considered related with respect to
recommendations based on the anchor visualization.
[0223] Next, in one or more of the various embodiments, control may
be returned to a calling process.
[0224] It will be understood that each block in each flowchart
illustration, and combinations of blocks in each flowchart
illustration, can be implemented by computer program instructions.
These program instructions may be provided to a processor to
produce a machine, such that the instructions, which execute on the
processor, create means for implementing the actions specified in
each flowchart block or blocks. The computer program instructions
may be executed by a processor to cause a series of operational
steps to be performed by the processor to produce a
computer-implemented process such that the instructions, which
execute on the processor, provide steps for implementing the
actions specified in each flowchart block or blocks. The computer
program instructions may also cause at least some of the
operational steps shown in the blocks of each flowchart to be
performed in parallel. Moreover, some of the steps may also be
performed across more than one processor, such as might arise in a
multi-processor computer system. In addition, one or more blocks or
combinations of blocks in each flowchart illustration may also be
performed concurrently with other blocks or combinations of blocks,
or even in a different sequence than illustrated without departing
from the scope or spirit of these innovations.
[0225] Accordingly, each block in each flowchart illustration
supports combinations of means for performing the specified
actions, combinations of steps for performing the specified actions
and program instruction means for performing the specified actions.
It will also be understood that each block in each flowchart
illustration, and combinations of blocks in each flowchart
illustration, can be implemented by special purpose hardware-based
systems, which perform the specified actions or steps, or
combinations of special purpose hardware and computer instructions.
The foregoing example should not be construed as limiting or
exhaustive, but rather, an illustrative use case to show an
implementation of at least one of the various embodiments of the
invention.
[0226] Further, in one or more embodiments (not shown in the
figures), the logic in the illustrative flowcharts may be executed
using an embedded logic hardware device instead of a CPU, such as,
an Application Specific Integrated Circuit (ASIC), Field
Programmable Gate Array (FPGA), Programmable Array Logic (PAL), or
the like, or combination thereof. The embedded logic hardware
device may directly execute its embedded logic to perform actions.
In one or more embodiments, a microcontroller may be arranged to
directly execute its own embedded logic to perform actions and
access its own internal memory and its own external Input and
Output Interfaces (e.g., hardware pins or wireless transceivers) to
perform actions, such as System On a Chip (SOC), or the like.
* * * * *