U.S. patent application number 14/098505 was filed with the patent office on 2014-06-26 for device, method and user interface for switching between graphical representations of data.
This patent application is currently assigned to Lithium Technologies, Inc.. The applicant listed for this patent is Lithium Technologies, Inc.. Invention is credited to Michael Wu.
Application Number | 20140181728 14/098505 |
Document ID | / |
Family ID | 50932491 |
Filed Date | 2014-06-26 |
United States Patent
Application |
20140181728 |
Kind Code |
A1 |
Wu; Michael |
June 26, 2014 |
Device, Method and User Interface for Switching between Graphical
Representations of Data
Abstract
A device with one or more processors and memory displays a first
representation of a first subset of data from a data set. The first
subset of data is associated with a first time period with more
than a threshold number of discrete time steps. The first
representation shows changes in the first subset of data over time.
The device receives a request to view data associated with a second
time period with no more than the threshold number of discrete time
steps. In response, the device displays a second representation of
a second subset of data from the data set. The second subset of
data is associated with the second time period. The second
representation aggregates the second subset of data over the second
time period and shows divisions of the data in a respective set of
one or more data-set dimensions.
Inventors: |
Wu; Michael; (Oakland,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Lithium Technologies, Inc. |
Emeryville |
CA |
US |
|
|
Assignee: |
Lithium Technologies, Inc.
Emeryville
CA
|
Family ID: |
50932491 |
Appl. No.: |
14/098505 |
Filed: |
December 5, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61734927 |
Dec 7, 2012 |
|
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Current U.S.
Class: |
715/781 |
Current CPC
Class: |
G06F 3/04842 20130101;
G06Q 50/01 20130101; G06F 3/0481 20130101; G06F 16/248 20190101;
G06F 16/904 20190101 |
Class at
Publication: |
715/781 |
International
Class: |
G06F 3/0481 20060101
G06F003/0481; G06F 3/0484 20060101 G06F003/0484 |
Claims
1. A method, comprising: at a computing device with a display:
displaying, in a data display panel, a first representation of a
first subset of data from a data set, wherein: respective data
items in the data set are associated with corresponding discrete
time steps of a plurality of time steps; the first subset of data
includes data associated with a first time period that includes
more than a threshold number of discrete time steps; and the first
representation is a first type of data visualization that shows
changes in the first subset of data over time; receiving a first
input corresponding to a request to view data associated with a
second time period that includes no more than the threshold number
of discrete time steps; and in response to receiving the first
input, displaying, in the data display panel, a second
representation of a second subset of data from the data set,
wherein: the second subset of data includes data associated with
the second time period; the second representation is a second type
of data visualization that aggregates the second subset of data
over the second time period and shows divisions of the data in a
respective set of one or more data-set dimensions; and the second
type of data visualization is different from the first type of data
visualization.
2. The method of claim 1, wherein the threshold number of time
steps is 1.
3. The method of claim 1, comprising: after displaying the second
representation of the second subset of the data from the data set,
receiving a second input corresponding to a request to view data
associated with a third time period that has no more that the
threshold number of discrete time steps; and in response to
receiving the second input, displaying, in the data display panel,
a third representation of a third subset of data from the data set,
wherein: the third subset of data includes data associated with the
third time period; and the third representation is the second type
of data visualization that aggregates the third subset of data over
the third time period and shows divisions of the data in the
respective set of one or more data-set dimensions.
4. The method of claim 1, wherein the size of the discrete time
steps is adjustable by user.
5. The method of claim 1, wherein the first type of data
visualization is a line graph.
6. The method of claim 1, wherein the second type of data
visualization is a treemap.
7. The method of claim 1, wherein the second representation shows
divisions of data along a plurality of data-set dimensions
simultaneously.
8. The method of claim 1, further comprising, after displaying the
second representation of the second subset of data: receiving a
third input corresponding to a request to display additional
information corresponding to a respective division that is visually
represented in the second representation; and in response to
receiving the third input, replacing display of at least a portion
of the second representation of the second subset of data with a
third representation of a third subset of data from the data set
that emphasizes subdivisions of the respective division and
corresponds to the second time period.
9. The method of claim 8, wherein the second representation of the
second subset of data includes an indication that the respective
division includes a plurality of subdivisions that can be viewed in
greater detail in the data display panel.
10. The method of claim 8, further comprising, after displaying the
third representation of the third subset of data: receiving a third
input corresponding to a request to display a fourth subset of data
from the data set that is associated with a third time period that
includes more than the threshold number of discrete time steps; and
in response to receiving the third input, displaying, in the data
display panel, a fourth representation of a fourth subset of data
from a data set, wherein: the fourth subset of data includes data
associated with the third time period; and the fourth
representation is the first type of data visualization that shows
changes in the fourth subset of data over time and data emphasizes
subdivisions of the respective division.
11. A computing device, comprising: one or more processors; and
memory; one or more programs, wherein the one or more programs are
stored in the memory and configured to be executed by the one or
more processors, the one or more programs including instructions
for: displaying, in a data display panel, a first representation of
a first subset of data from a data set, wherein: respective data
items in the data set are associated with corresponding discrete
time steps of a plurality of time steps; the first subset of data
includes data associated with a first time period that includes
more than a threshold number of discrete time steps; and the first
representation is a first type of data visualization that shows
changes in the first subset of data over time; receiving a first
input corresponding to a request to view data associated with a
second time period that includes no more than the threshold number
of discrete time steps; and in response to receiving the first
input, displaying, in the data display panel, a second
representation of a second subset of data from the data set,
wherein: the second subset of data includes data associated with
the second time period; the second representation is a second type
of data visualization that aggregates the second subset of data
over the second time period and shows divisions of the data in a
respective set of one or more data-set dimensions; and the second
type of data visualization is different from the first type of data
visualization.
12. The device of claim 11, wherein the threshold number of time
steps is 1.
13. The device of claim 11, wherein the one or more programs
include instructions for: after displaying the second
representation of the second subset of the data from the data set,
receiving a second input corresponding to a request to view data
associated with a third time period that has no more that the
threshold number of discrete time steps; and in response to
receiving the second input, displaying, in the data display panel,
a third representation of a third subset of data from the data set,
wherein: the third subset of data includes data associated with the
third time period; and the third representation is the second type
of data visualization that aggregates the third subset of data over
the third time period and shows divisions of the data in the
respective set of one or more data-set dimensions.
14. The device of claim 11, wherein the first type of data
visualization is a line graph.
15. The device of claim 11, wherein the second type of data
visualization is a treemap.
16. A non-transitory computer readable storage medium storing one
or more programs, the one or more programs comprising instructions,
which when executed by a computing device with one or more
processors cause the device to: displaying, in a data display
panel, a first representation of a first subset of data from a data
set, wherein: respective data items in the data set are associated
with corresponding discrete time steps of a plurality of time
steps; the first subset of data includes data associated with a
first time period that includes more than a threshold number of
discrete time steps; and the first representation is a first type
of data visualization that shows changes in the first subset of
data over time; receiving a first input corresponding to a request
to view data associated with a second time period that includes no
more than the threshold number of discrete time steps; and in
response to receiving the first input, displaying, in the data
display panel, a second representation of a second subset of data
from the data set, wherein: the second subset of data includes data
associated with the second time period; the second representation
is a second type of data visualization that aggregates the second
subset of data over the second time period and shows divisions of
the data in a respective set of one or more data-set dimensions;
and the second type of data visualization is different from the
first type of data visualization.
17. The non-transitory computer readable storage medium of claim
16, wherein the threshold number of time steps is 1.
18. The non-transitory computer readable storage medium of claim
16, wherein the one or more programs include instructions which,
when executed by the computing device, cause the device to: after
displaying the second representation of the second subset of the
data from the data set, receive a second input corresponding to a
request to view data associated with a third time period that has
no more that the threshold number of discrete time steps; and in
response to receiving the second input, display, in the data
display panel, a third representation of a third subset of data
from the data set, wherein: the third subset of data includes data
associated with the third time period; and the third representation
is the second type of data visualization that aggregates the third
subset of data over the third time period and shows divisions of
the data in the respective set of one or more data-set
dimensions.
19. The non-transitory computer readable storage medium of claim
16, wherein the first type of data visualization is a line
graph.
20. The non-transitory computer readable storage medium of claim
16, wherein the second type of data visualization is a treemap.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data" which is incorporated
herein by reference in its entirety.
[0002] This application is related to the following applications:
U.S. patent application Ser. No. ______, "Device, Method and User
Interface for Presenting Analytic Data," filed ______, (Attorney
Docket No. 070407-5001US); U.S. patent application Ser. No. ______,
"Device, Method and User Interface for Determining a Correlation
between a Received Sequence of Numbers and Data that Corresponds to
Metrics," filed ______, (Attorney Docket No. 070407-5009US); U.S.
patent application Ser. No. ______, "Device, Method and User
Interface for Displaying Relationships between Different Sets of
Data," filed ______, (Attorney Docket No. 070407-5010US); and U.S.
patent application Ser. No. ______, "Device, Method and User
Interface for Emphasizing Divisions in Data," filed ______,
(Attorney Docket No. 070407-5012US), which are incorporated by
reference herein in their entirety.
TECHNICAL FIELD
[0003] The disclosed implementations relate generally to methods of
data visualization.
BACKGROUND
[0004] An online community is a website designed for users to
interact with each other, usually with some common theme. Unlike a
traditional website, in which the website owner controls all of the
content, an online community enables and encourages users to
participate in the content. Users post comments, replies to
comments, questions, and answers to other users' questions; more
experienced users develop articles and knowledge bases, and lead
forum discussions or blogs.
[0005] Business entities now recognize the value of having an
online community for the business. In this case, the community
focus is on the products or services of the business, and users
participate in the community just like any other online community.
While online communities can be beneficial for marketing, online
communities are not just a marketing gimmick. For example, real
users post real questions, and the questions are frequently
answered by other users in the community. Of course an online
community is much more than just a question and answer board.
SUMMARY
[0006] It is import to measure the success and "health" of an
online community. To make these measurements, an abundance of data
is tracked about user interactions with the community. Every
interaction is tracked, as well as information about the
interaction, such as where it originated, what time, what type of
computing device the user was using, the content of the interaction
itself (such as a post), as well as subsequent responses to the
interaction, such as other users designating the comment or answer
as useful. This abundance of data is almost too much, and previous
methods of reviewing the data have been cumbersome or ineffective.
Because of the shortcoming of previous attempts, implementations of
the present invention provide simpler and more effective ways of
reviewing interaction data for an online community.
[0007] In some implementations, a method includes, at a computing
device with a display, displaying, in a data display panel, a first
representation of a first subset of data from a data set.
Respective data items in the data set are associated with
corresponding discrete time steps of a plurality of time steps. The
first subset of data includes data associated with a first time
period that includes more than a threshold number of discrete time
steps. The first representation is a first type of data
visualization that shows changes in the first subset of data over
time. The method further includes receiving a first input
corresponding to a request to view data associated with a second
time period that includes no more than the threshold number of
discrete time steps, and, in response to receiving the first input,
displaying, in the data display panel, a second representation of a
second subset of data from the data set. The second subset of data
includes data associated with the second time period. The second
representation is a second type of data visualization that
aggregates the second subset of data over the second time period
and shows divisions of the data in a respective set of one or more
data-set dimensions. The second type of data visualization is
different from the first type of data visualization.
[0008] In accordance with some embodiments, a computer system
(e.g., a search client system or search server system) includes one
or more processors, memory, and one or more programs; the one or
more programs are stored in the memory and configured to be
executed by the one or more processors and the one or more programs
include instructions for performing the operations of the method
described above. In accordance with some embodiments, a
non-transitory computer readable storage medium has stored therein
instructions which when executed by one or more processors, cause a
computer system (e.g., a search client system or search server
system) to perform the operations of the methods described
above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a conceptual block diagram of an online community
in accordance with some implementations.
[0010] FIG. 2 is a block diagram of a client device in accordance
with some implementations.
[0011] FIG. 3 is a block diagram illustrating the structure of web
servers, database servers, and application servers in accordance
with some implementations.
[0012] FIGS. 4A-4C are flow diagrams illustrating a method of
switching between data visualizations based on time period for data
in a data viewing interface in accordance with some
implementations.
[0013] FIGS. 5A-5X illustrate methods of presenting, selecting, and
changing views of data in accordance with some implementations.
[0014] FIG. 6 illustrates an example of a data flow and
architecture for collection of analytics data for an online
community and presentation of analytic data for that online
community in accordance with some implementations.
[0015] Like reference numerals refer to corresponding parts
throughout the several views of the drawings.
DESCRIPTION OF IMPLEMENTATIONS
[0016] Reference will now be made in detail to implementations,
examples of which are illustrated in the accompanying drawings. In
the following detailed description, numerous specific details are
set forth in order to provide a thorough understanding of the
present invention. However, it will be apparent to one of ordinary
skill in the art that the present invention may be practiced
without these specific details. In other instances, well-known
methods, procedures, components, and circuits have not been
described in detail so as not to unnecessarily obscure aspects of
the implementations.
[0017] FIG. 1 illustrates an example of an environment in which
disclosed implementations operate. In the middle is one or more
communication networks 112, such as the Internet, that link
together the various computing devices. Users 102 are individual
people who access the communication networks 112 using electronic
devices 104, such as desktop computers, laptop computers, tablet
computers, smartphones, PDA's, etc. The communication networks are
also connected to one or more Social Media sites 106, such as
Facebook, Youtube, etc.
[0018] Also connected to the communication network 112 is an online
community 100 for a business entity. An online community 100
provides public forums, bulletin boards, blogs, and other
information. Unlike a traditional business website, an online
community is maintained by everyone, including the users 102. Users
102, for example, can post questions about products or services
provided by the business entity, or can post answers to other users
questions. An online community can increase profitability of the
business entity in many ways, including reducing the costs for
customer support (users find the information in the community) and
reducing the cost of search engine optimization (for example,
because search engines review the user generated community
content).
[0019] An online community 100 includes one or more web servers 114
that handle the incoming web requests, and one or more application
server 116 that provide the user interface functionality for the
community. The online community 100 also includes one or more
database servers 118 that store the data for the community,
including logs of user interactions. In some implementations, the
database servers 118 also store binary files, image files, video
files, and so on. In some implementations, binary, image, and video
files are stored on the application servers 116, of other file
servers (not shown).
[0020] In addition to the users 102 who interact directly with the
online community 100, managers, stakeholders, and executives 108 of
the business entity review the community interaction data. As
described in more detail below, some people (e.g., managers,
stakeholders and/or executives) 108 review the raw data, and other
people 108 review analytic computed data. The people 108 utilize
client devices 110 to access the interaction data over the
communication network 112. Like a user device, a client devices can
be desktop computers, laptop computers, tablet computers,
smartphones, PDA's, etc.
[0021] Although FIG. 1 illustrates Web Servers 114, application
servers 116, and database servers 118 as physically distinct
devices, the functionality can be combined in various different
ways. For example, the web server software and application server
software optionally runs on the same server(s).
[0022] FIG. 2 illustrates an example of a client device 110. A
client device 110 generally includes one or more processing units
(CPUs) 202, one or more network or other communications interfaces
204, memory 214, and one or more communication buses 212 for
interconnecting these components. The communication buses 212
optionally include circuitry (sometimes called a chipset) that
interconnects and controls communications between system
components. A client device 110 also includes a user interface 206,
for instance a display 208 and a keyboard or other input mechanism
210. Some client devices 110 display a keyboard 210 on the display
208 as needed. Some client devices 110 utilize a touchscreen or
touch-sensitive display 208. Memory 214 optionally includes
high-speed random access memory, such as DRAM, SRAM, DDR RAM or
other random access solid state memory devices; and optionally
includes non-volatile memory, such as one or more magnetic disk
storage devices, optical disk storage devices, flash memory
devices, or other non-volatile solid state storage devices. Memory
214 optionally includes mass storage that is remotely located from
the central processing unit(s) 202. Memory 214, or alternately the
non-volatile memory device(s) within memory 214, comprises a
computer readable storage medium. In some implementations, memory
214 or the computer readable storage medium of memory 214 stores
the following programs, modules and data structures, or a subset
thereof: [0023] an operating system 216 (e.g., Windows, Mac OS X,
iOS, Android, or Linux) that generally includes procedures for
handling various basic system services and for performing hardware
dependent tasks; [0024] a network communications module 218 that is
used for connecting the client device 110 to servers or other
computing devices via one or more communication networks, such as
the Internet, other wide area networks, local area networks,
metropolitan area networks, and the like; [0025] a web browser 220
(e.g., Internet Explorer, Safari, Chrome) that is used to access
web pages, web applications, and other resources over a
communication network, such as the Internet, other wide area
networks, local area networks, metropolitan area networks, and the
like; [0026] a metric explorer interface 5000, enabling a user to
graphically view data from user interaction with an online
community or other social media, and to select many different
aspects (or dimensions) of what data is viewed, and how the data is
viewed. The metric explorer interface is described in more detail
with reference to FIGS. 5A-5X below and FIGS. 9-005 to 9-141 of
U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7,
2012, entitled "Systems and Methods for Presenting Analytic Data";
[0027] an actionable analytics interface 222, enabling a user to
graphically review calculated analytic data based on users'
interactions with an online community or other social media. The
actionable analytics interface 222 is described in more detail with
reference to FIGS. 9-142 to 9-206 of U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data"; [0028] a return on
investment (ROI) calculation interface 224, enabling a user to
compute the return on investment for an online community 100, or
compute correlations between business key performance indicators
(KPIs) and various metrics. The ROI calculation interface 224 is
described in greater detail with reference to FIGS. 9-207 to 9-236
of U.S. Provisional Patent Application No. 61/734,927, filed Dec.
7, 2012, entitled "Systems and Methods for Presenting Analytic
Data"; and [0029] one or more browser cookies 226, which save state
or other information for a web page or web application.
[0030] FIG. 3 illustrates an example of a server, such as a web
server 114, an application server 116, or a database server 118. A
server 114/116/118 generally includes one or more processing units
(CPUs) 302, one or more network or other communications interfaces
304, memory 314, and one or more communication buses 312 for
interconnecting these components. The communication buses 312
optionally include circuitry (sometimes called a chipset) that
interconnects and controls communications between system
components. The server 114/116/118 optionally includes a user
interface 306, for instance, a display 308 and a keyboard or other
input device 310. Memory 314 optionally includes high-speed random
access memory, such as DRAM, SRAM, DDR RAM or other random access
solid state memory devices; and optionally includes non-volatile
memory, such as one or more magnetic disk storage devices, optical
disk storage devices, flash memory devices, or other non-volatile
solid state storage devices. Memory 314 optionally includes mass
storage that is remotely located from the central processing
unit(s) 302. Memory 314, or alternately the non-volatile memory
device(s) within memory 314, comprises a computer readable storage
medium. In some implementations, memory 314 or the computer
readable storage medium of memory 314 stores the following
programs, modules and data structures, or a subset thereof: [0031]
an operating system 316 (e.g., Linux or Unix) that generally
includes procedures for handling various basic system services and
for performing hardware dependent tasks; [0032] a network
communications module 318 that is used for connecting the server
114/116/118 to other servers or other computing devices via one or
more communication networks, such as the Internet, other wide area
networks, local area networks, metropolitan area networks, and the
like; [0033] web server software 320 (e.g. Apache web server or
Apache Tomcat) that receives web requests, and delivers appropriate
web pages or other resources in response to the requests; [0034]
database server software 322 (e.g., MySQL or other structured query
language (SQL) database engine), which stores organized relational
data for the online community 100, and provides the data as needed;
[0035] a database 324 that contains one or more community
interaction logs. The log database(s) track information about user
interactions with the online community. For example, clicking to
view a web page generates a "page view" event, which is logged. The
log record for this event optionally includes the user name or ID,
the time of the page view, which page was viewed, etc.; [0036] a
forum database 326, which includes data for one or more community
forums. This includes all data and metadata for posts to each
forum, as well as configuration information about each forum;
[0037] a blog database 328, which stores blogs for one or more
individuals, including all of the content of the blog, as well as
comments, etc.; [0038] a tribal knowledge base database 330, which
is an organized collection of articles that have generally
proceeded through a review process, providing useful information on
a topic (e.g., how to utilize a feature of a product provided by
the business entity); [0039] an application server 332, which
provides the functionality of the online community 100 and
functionality to access the interaction data 324 generated by users
102 of the online community 100; [0040] one or more community
interaction applications or interfaces 334, which are used by users
102 who participate in the online community 100; [0041] a metric
explorer interface 5000, which is used by users (e.g., Managers) to
review the raw interaction data. This is described in more detail
with reference to FIGS. 5A-5X below and FIGS. 9-005 to 9-141 of
U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7,
2012, entitled "Systems and Methods for Presenting Analytic Data";
[0042] an actionable analytics interface 222, which is used by
users (e.g., Stakeholders) to review computed analytic data based
on the interactions of users 102 with the online community 100. The
actionable analytics interface 222 is described in more detail with
reference to FIGS. 9-142 to 9-206 of U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data"; [0043] a KPI/ROI
calculation interface 224, which is used by users (e.g.,
Executives) to evaluate the return on investment for an online
community. In some implementations, the interface 224 computes
correlations between business key performance indicators and
community metrics. This is described in greater detail with
reference to FIGS. 9-207 to 9-236 of U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data"; [0044] a KPI calculation
engine 336, which computes correlation coefficients between sets of
data; [0045] a data export application 338, which converts
retrieved data into various external file formats, including CSV
(comma separated values), PDF (portable document format), PNG
(portable network graphics), and XLS (Microsoft Excel).
[0046] Although FIG. 3 shows examples of servers, it is intended
more as functional description of the various features which are,
in some circumstances, present in a set of servers than as a
structural schematic of the implementations described herein. In
practice, and as recognized by those of ordinary skill in the art,
items shown separately could be combined and some items could be
separated. For example, some items shown separately in FIGS. 1 and
3 could be implemented on single servers and single items could be
implemented by one or more servers. The actual number of servers
used to implement the web servers, application servers, and
database servers, and how features are allocated among them will
vary from one implementation to another, and optionally depends in
part on the amount of data traffic that the system must handle
during peak usage periods as well as during average usage
periods.
[0047] Each of the methods described herein are, optionally,
performed by instructions that are stored in a computer readable
storage medium and that are executed by one or more processors of
one or more servers or clients. Each of the operations shown in
FIGS. 4A-4C optionally correspond to instructions stored in a
computer memory or computer readable storage medium.
Methods for Displaying Data
[0048] FIGS. 4A-4C are flow diagrams illustrating a method 400 of
switching between data visualizations based on time period for data
in a data viewing interface in accordance with some
implementations. The method 400 is performed at a computing device
(e.g., Client Device 110 in FIG. 2, Web Server 114 in FIG. 3,
Application Server 116 in FIG. 3, or Database Server 118 in FIG. 3)
with a display and an input device (e.g., a mouse, a trackpad, a
touch screen or voice control module). Some operations in method
400 re, optionally, combined and/or the order of some operations
is, optionally, changed. As described below, the method 400
provides an intuitive way to switch between data visualizations
based on time period for data in a data viewing interface. The
method reduces the cognitive burden on a user when viewing data,
thereby creating a more efficient human-machine interface. For
battery-operated electronic devices, enabling a user to view data
faster and more efficiently conserves power and increases the time
between battery charges.
[0049] In some implementations, the device displays (402), in a
data display panel, a first representation of a first subset of
data from a data set. Respective data items in the data set are
(404) associated with corresponding discrete time steps of a
plurality of time steps. The first subset of data includes (406)
data associated with a first time period that includes more than a
threshold number of discrete time steps. In some implementations,
the threshold number of time steps is (408) 1. In some
implementations the threshold number of time steps is (410) an
integer greater than 1 (e.g., 2, 3, 4, 5, 10 or some other
reasonable number). In some implementations, the size of the
discrete time steps is (412) adjustable by user. The first
representation is (414) a first type of data visualization that
shows changes in the first subset of data over time (e.g., as shown
in FIGS. 5A-5I). In some implementations, the first type of data
visualization is (416) a line graph (e.g., as shown in FIGS.
5A-5I).
[0050] After (e.g., while) displaying the first representation of
the first subset of the data, the device receives (418) a first
input (e.g., from the input device) corresponding to a request to
view data associated with a second time period that includes no
more than the threshold number of discrete time steps. In response
to receiving the first input, the device displays (420), in the
data display panel, a second representation of a second subset of
data from the data set. The second subset of data includes (422)
data associated with the second time period. The second
representation is (424) a second type of data visualization that
aggregates the second subset of data over the second time period
and shows divisions of the data in a respective set of one or more
data-set dimensions (e.g., data-set dimensions other than the
dimension of time). The second type of data visualization is (426)
different from the first type of data visualization (e.g., as shown
in FIGS. 5J-5V).
[0051] In some implementations, the second representation shows
(428) divisions of data along a plurality of data-set dimensions
simultaneously (e.g., via size of box, shading, color, saturation,
tone/topic hierarchy, number of posts, topics/node structure,
number of posts, sentiment or other dimensions, as described herein
with reference to FIGS. 5W-5X). In some implementations, the second
type of data visualization is (430) a treemap (e.g., as shown in
FIGS. 5J-5V).
[0052] In some implementations, after displaying the second
representation of the second subset of the data from the data set,
the device receives (432) a second input corresponding to a request
to view data associated with a third time period that has no more
that the threshold number of discrete time steps. In response to
receiving the second input, the device displaying (434), in the
data display panel, a third representation of a third subset of
data from the data set. In some implementations, the third subset
of data includes (436) data associated with the third time period;
and the third representation is (438) the second type of data
visualization that aggregates the third subset of data over the
third time period and shows divisions of the data in the respective
set of one or more data-set dimensions (e.g., as shown in FIGS.
5M-5T). In some of implementations, where second type of data
visualization is a treemap (e.g., as shown in FIGS. 5J-5V),
switching from the second representation to the third
representation includes changing the values shown in the treemap
without redrawing the treemap to show the changes in the relative
quantity of data in the different divisions (e.g., as shown in
FIGS. 5R-5S). In some implementations, where second type of data
visualization is a treemap (e.g., as shown in FIGS. 5J-5V),
switching from the second representation to the third
representation includes redrawing the treemap to show the changes
in the relative quantity of data in the different divisions (e.g.,
as shown in FIGS. 5R-5T).
[0053] In some implementations, after displaying the second
representation of the second subset of data the device receives
(446) a third input corresponding to a request to display
additional information corresponding to a respective division that
is visually represented in the second representation. In some
implementations, the second representation of the second subset of
data includes (442) an indication that the respective division
includes a plurality of subdivisions that can be viewed in greater
detail in the data display panel. For example, the section of the
treemap corresponding to the respective division shows subdivisions
of the respective division, optionally as subdivisions of the
section of the treemap that correspond to the relative amounts of
data corresponding to the subdivisions, as shown in elements 5134
and 5136 in FIG. 5J and element 5146 in FIG. 5K. In some
implementations, in response to receiving the third input, the
device replaces (444) display of at least a portion of the second
representation of the second subset of data with a third
representation of a third subset of data from the data set that
emphasizes subdivisions of the respective division and corresponds
to the second time period (e.g., as shown in FIGS. 5J-5K and FIGS.
5K-5L).
[0054] In some implementations, after displaying the third
representation of the third subset of data the device receives
(446) a third input corresponding to a request to display a fourth
subset of data from the data set that is associated with a third
time period that includes more than the threshold number of
discrete time steps. In some implementations, in response to
receiving the third input, displaying (448), in the data display
panel, a fourth representation of a fourth subset of data from a
data set, where the fourth subset of data includes (450) data
associated with the third time period, and the fourth
representation is (452) the first type of data visualization that
shows changes in the fourth subset of data over time and data
emphasizes subdivisions of the respective division. For example,
the third representation is a representation of subdivisions of the
respective division aggregated over the second time period (e.g.,
one time step) and the fourth representation is a line graph that
illustrates changes in data corresponding to the subdivisions of
the respective division over the third time period (e.g., a
plurality of time steps).
[0055] It should be understood that the particular order in which
the operations in FIGS. 4A-4C have been described is merely an
example and is not intended to indicate that the described order is
the only order in which the operations could be performed. One of
ordinary skill in the art would recognize various ways to reorder
the operations described herein. Additionally, it should be noted
that details of other processes described in U.S. Provisional
Patent Application No. 61/734,927, filed Dec. 7, 2012, entitled
"Systems and Methods for Presenting Analytic Data" with respect to
other methods described therein (e.g., methods 400, 500, 700 and
800 in U.S. Provisional Patent Application No. 61/734,927) are also
applicable in an analogous manner to method 400 described above
with respect to FIGS. 4A-4C. For example, the inputs, data display
panels, data selection interfaces, and data-set dimensions
described above with reference to method 400 optionally have one or
more of the characteristics of the inputs, data display panels,
data selection interfaces, and data-set dimensions described in
U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7,
2012, entitled "Systems and Methods for Presenting Analytic Data"
with reference to other methods described therein (e.g., methods
400, 500, 700 and 800 in U.S. Provisional Patent Application No.
61/734,927). For brevity, these details are not repeated here.
[0056] Participation in an online community or other social media
generates a lot of data. For example, when a user posts a question,
comment, or answer, the post itself is recorded, as well as the
date/time of the post, who made the post, etc. Different people
related to the community or social media are interested in
different aspects of the data. In some implementations, the parties
who review the data (e.g., users of the device described above) are
categorized into three roles, including Managers, Stakeholders, and
Executives.
[0057] A manager is someone who participates in the day-to-day
management of an online community platform. Manager reviews the
direct measurements of participation by community members. For
example, a manager would review raw metrics such as the total
number of posts, the total number of posts of specific types (e.g.,
questions, answers), the number of fans, the number of discussion
threads and the lengths of those threads, the number of times
particular content is viewed by users, the comments or ratings
received by each particular user and/or posts, and so on. These
quantities are directly measurable by clicks, button presses, and
other recorded user interactions, and constitute level 1 data. In
general, the level 1 data reviewed by managers comprises raw
metrics and simple reports based on the raw metrics.
[0058] A business stakeholder is someone who derives business value
from the online community. Business stakeholders are less
interested in the raw metrics collected about a community, and are
more interested in data that shows the success or effectiveness of
an online community or other social media. For example, a
stakeholder reviews market share or share of the advertising in the
market, or other data that demonstrates how well the media content
resonates with the participants of the community. The data of
interest to the business stakeholders are actionable analytics
generated from the raw metrics. Various statistical and analytical
techniques can be used to generate the analytics from the raw
metrics. Frequently the actionable analytics have complex and
non-linear relationships with the raw metrics from which the
analytics are derived. The level 2 data reviewed by business
stakeholders comprises actionable analytics.
[0059] Finally, business executives review data that ties social
media directly to business objectives. Generally, the business
objectives are financial, such as revenue or profit, but also
include less tangible objectives such as customer satisfaction. The
level 3 data reviewed by business executives comprise key
performance indicators (KPI's), return on investment (ROI), and so
on. The level 3 data is derived from the raw metrics and the
actionable analytics associated with the community, as well as
additional data (e.g., returns, revenues, and customer satisfaction
metrics) provided by the entity using the community to promote its
business objectives.
[0060] FIGS. 5A-5X below describe a user interface displayed on a
display that is coupled to a computing device with one or more
processors and memory that store programs that are configured to be
executed by the one or more processors. Below, when a user is
described as performing an operation associated with a displayed
user interface (e.g., selecting an option, activating an
affordance, or the like), the computer is detecting an input
provided by the user (e.g., using a user input device such as a
mouse, a touchpad, a keyboard, voice commands, etc.), and the
computing device is responding to the detected input by updating
the user interface and/or performing an associated operation in
accordance with the detected input. Thus, for example, when a user
"selects a time range to be displayed," the device is detecting an
input generated by a user with an input device (e.g., a mouse,
trackpad or keyboard) that corresponds to selecting the time range,
and the device is responding to the input that corresponds to
selecting the time range by selecting the time range.
[0061] FIGS. 5A-5V illustrate various implementations that provide
a simpler and more effective user interface for displaying level 1
data and level 2 data. The level 1 data and the level 2 data are
examples of a multi-dimensional data set, where data units within
the multi-dimensional data set can be selectively displayed in the
user interface using various data selection interfaces (or data
selectors). In some implementations, each data set dimension
represents a respective aspect of the data set. For example,
dimensions of the data set can be related to the "who", "what",
"when", and "where" aspects of the data set, respectively. In some
implementations, each data set dimension further includes multiple
"divisions", where each division represents a filter for a subset
of the available data. For example, divisions in the "who"
dimension can include various categories, roles, and/or ranks of
users. Divisions in the "what" dimension can include various types
of raw metrics and analytics (collectively called "metrics").
Divisions in the "what" dimension can be further filtered or
limited by a "how" dimension, which provides attributes or context
for the data in the "what" dimension. Divisions in the "when"
dimension can include various time periods and/or time ranges.
Divisions in the "where" dimension can include various sources or
channels from which the data is collected. For example, the "where"
dimension can include various channels (e.g., community, various
other third-party social media sites, and so on) from which the
metrics data is collected. Sometimes, the "where" dimension also
include subdivisions representing sub-topics and/or sub-communities
within a community or data channel. The "who", "what", "when", and
"where" data-set dimensions are merely illustrative for the kinds
of data-set dimensions that can be represented by the data
selection interfaces shown below. Not all dimensions need to be
present in a data set, and not all dimensions present in the data
set need to have a respective data selection interface in various
implementations.
[0062] FIGS. 5A-5G illustrate graphs in the data display panel 5028
with various channel selections. In some implementations, when no
specific forum or node has been selected, the data display panel is
empty as illustrated in FIG. 5A where only axes are displayed in
the data display panel. In some implementations, when no specific
forum or node is selected, the graph 5116 displays data for the
entire community, as illustrated in FIG. 5B.
[0063] In FIG. 5C, the "community Suggestions" node or forum 5118
has been selected in the channel selector interface, so the graph
5124 (e.g., a graph of the number of posts in the "Community
Suggestions" forum of the "Li" community over time) corresponding
to the node 5118 is presented in the data display panel 5028. In
this implementation, the blue color of the graph 5124 corresponds
to the blue highlighting of the "Community Suggestions" node 5118.
In FIG. 5D, the node "Show us your Home Theater!" 5122 is selected
in addition to the "Community Suggestions" node, and graph 5126
(e.g., a graph of the number of posts in the "Show us your Home
Theater!" forum of the "Li" community over time) corresponding to
the node 5122 displays in the data panel 5028. The green color of
the graph 5126 corresponds to the green highlighting of the node
5122. As noted previously with the selection of users, some
implementations use shading, patterns, or labels to correlate
graphs with selected options (in this case, nodes or sub-domains).
In FIG. 5E, the "Community Suggestions" node 5118 has been
unselected, so only the graph 5126 corresponding to the "Show us
your Home Theater" node 5122 is displayed in the data display panel
5028. Comparison of FIG. 5D to FIG. 5E illustrates that some
implementations adjust the range on the y-axis based on the data
range(s) for the selected graphs(s). Whereas graph 5124 required
the larger range of 10 to 70, graph 5126 can be displayed in the
smaller range of 15 to 45. In FIG. 5E, the graph 5126 extends more
vertically because of the change in scaling of the y-axis. In FIG.
5F, the "Community Suggestions" node 5118 has been reselected, and
thus graphs 5124 and 5126 both appear in the data display panel
5028 (as in FIG. 5D).
[0064] In FIG. 5G, the "HiFi Ideas" node 5120 has been selected in
addition to the nodes 5118 and 5122, so three graphs 5124, 5126,
and 5128 are displayed in the data display panel 5028. The orange
color of the graph 5128 (e.g., a graph of the number of posts in
the "HIFI Ideas" forum of the "Li" community over time) corresponds
to the orange highlighting of the "HiFi Ideas" node 5120. Because
the post counts for the "HiFi Ideas" node 5120 are much greater
than the post counts for the other two nodes, the device (e.g.,
device 110) displaying the user interface shown in FIG. 5G has
adjusted the scale on the y-axis. The graphs 5124 and 5126 are the
same as in FIG. 5, but are compressed vertically so that they can
appear with the graph 5128.
[0065] FIGS. 5A-5G illustrate variations in the channel selection
without changing any of the other selectors. Each of these figures
uses the "post count" metric, with no specific attribute
selections, for the time period mid July, 2010 to mid November,
2010, and includes data for all users.
[0066] FIGS. 5H-5I illustrate how data is displayed in the data
panel 5028 when a user selects multiple nodes in the channel
selector 5018 and multiple users (or multiple groups of users using
ranks or roles) in the user selector 5026. As explained earlier,
selection of multiple users or user groups generally results in a
graph for each of the users or groups of users; similarly,
selection of multiple nodes in the channel selector generally
results in a graph for each of the nodes. In some implementations
(not illustrated), there is a separate graph for each (node, user
selection) combination. In the example in FIG. 5H, there are three
selected nodes in the channel selector, and three selected user
roles in the user selector, so nine distinct (node, user role)
graphs are optionally displayed in the data display panel 5028.
Because each graph corresponds to a node/role combination, colors,
shading, and patterns are insufficient to identify a combination.
In some implementations, each graph is given a label, which
identifies a respective combination of options selected in two
different dimensions (e.g., the channel and user dimensions).
[0067] Creating a separate graph for each (node, user selection)
combination is both visually hard to understand, and generally does
not correspond to what a user wants to see. Instead, preferred
implementations enable grouping of the selected data. For example,
if a user wants a separate graph for each selected role, then the
data for the selected nodes can be grouped together for each
selected role. Conversely, if the user wants a graph for each of
the nodes, then the data for all of the selected roles can be
grouped together for each of the selected nodes. In some
implementations, this is implemented using a "Group By" arrow
button 5132 (i.e., an example of a dimension selector). In some of
some implementations, there is a label "Group By" 5130 adjacent to
the arrow button 5132 to provide more information to the user using
the user interface shown in FIGS. 5H-5I.
[0068] As will be shown in later figures, when the group by arrow
5132 points to the user selector 5026, there is a single graph for
each of the selected roles. The data for each graph is limited to
the selected nodes rather than using data for the entire
community.
[0069] Similarly, as will be shown in later figures, when the group
by arrow 5132 points to the channel selector 5018, as illustrated
in FIG. 5I, there is a single graph for each of the selected nodes.
The data for each graph is limited to the selected users (e.g.,
selected roles "Advanced Studio," "BlogAuthor," and
"HiFi_Employee.")
[0070] In some implementations the "Group By" label 5130 and group
by arrow 5132 only appear when there are multiple selections for
both nodes and users/user groups.
[0071] FIGS. 5J-5V illustrate various implementations of a user
interface for the display of data when the selected time range is
just one unit of time (i.e., the size of the time range equals the
size of one time grain). In some implementations, the user
interface elements correspond to those of prior figures, including
a data display panel (e.g., the data display panel 5028), various
data selection interfaces (e.g., the channel selector 5018, metric
selectors 5020, 5022, time range selector 5024, and the user
selector 5026), a time grain selector 5054, lower and upper time
selection sliders 5048, 5050 and a time range indicator 5052. In
the preceding figures, the time grain has been "day," but in FIGS.
5J-5V, the time grain selector 5054 is set to "month" as
illustrated in FIG. 5J. Although not illustrated in FIG. 5J, when
the time grain selector 5054 is change to a larger grain (e.g.,
"day" to "month"), the sliders 5048 and 5050 automatically align or
"snap" to the nearest position that aligns with the new grain. For
example, with a time grain of "month," the sliders 5048 and 5050
optionally align with month boundaries in order to select entire
months.
[0072] The alignment of the sliders 5048 and 5050 with the time
grain is shown in FIG. 5J, where the time range selection is just
September, 2010 (a single month). In this case, there is only one
unit of time, so it would be not be useful to display a time-series
graph with a single point. Instead, some implementations switch the
display type from a line graph to a treemap or bar graph, depending
on the selections in the community selector 5018 and user selector
5026. For example, in some implementations, when the user selector
is set to "all users," the device (e.g., device 110) displaying the
user interface presents a treemap, as illustrated in FIG. 5J. In
this case, the data for all users is included, and broken into
counts for the top level nodes in the community. As Seen in FIG.
5J, the ten rectangles "Hi-Fi Products" 5134, "Engage with Hi-Fi"
5136, "Home Theater Q&A" 5138, etc. correspond to the top level
nodes in the node list 5076. The size of each rectangle and the
number below the node name indicate the number of posts to that
node for all users in September, 2010 (corresponding to the time
range selection). In some implementations, the use of two (or more)
different fill colors for the rectangles indicates that some of the
top level nodes include subnodes. For example, the top level node
"Hi-Fi Products" 5134 is a node with subnodes, so the device (e.g.,
device 110) displaying the user interface shown in FIG. 5J the node
5134 in a dark blue fill color, whereas the top level node "Home
Theater Q&A" 5138 has no subnodes, so the device (e.g., device
110) displaying the user interface shown in FIG. 5J the node 5138
with a light blue fill color. In some implementations, nodes that
have subnodes include outlines of the subnodes, as illustrated in
the "Hi Fi Products" node 5134.
[0073] When a node (such as "Hi-Fi Products" node 5134 in FIG. 5J)
has subnodes, a user can drill-down to see the detail, as shown in
FIG. 5K. In some implementations, double-clicking on node 5134
brings up the detail, as shown in FIG. 5K. There are various user
interface mechanisms that are, optionally, employed to bring up the
detail. For example, in response to a user having selected the
"Hi-Fi Products" node 5134 shown in FIG. 5J, the "Hi-Fi Products"
node indicator 5144 is displayed in the node selector 5018, as
illustrated in FIG. 5K. In FIG. 5K, another treemap is displayed,
but this time, in response to selection of the "Hi-Fi Products"
node 5144, the display is limited to data for that node. The Hi-Fi
Products node includes 15 subnodes, each displayed as a rectangle
in the treemap, including subnodes "Home Theater" 5140 and
"Speakers" 5142. Although drilling down has limited the data to the
specific "Hi-Fi Products" node 5144, the other selectors remain the
same. The user selection still has the value, "all users," the time
range selector still has the range September, 2010, and the metric
selector is still set to "post count" without selection of any
particular metric attributes. As illustrated in FIGS. 5J and 5K,
the total post count for node 5134 (1034 posts) is equal to the sum
of the post counts for all of the subnodes
(149+127+124+113+111+85+73+65+61+34+30+28+13+11+10).
[0074] FIG. 5K shows that the node "MP3 Players" 5146 has subnodes.
This is illustrated both by the dark blue color of node 5146 and
the outlines of subrectangles within the node. Double clicking on
the node 5146 causes further drill-down detail to be displayed, as
illustrated in FIG. 5L. In this Figure, The "MP3 Player" node 5146
is expanded to show the three subnodes "iPod Touch" 5148, "iPod
Nano" 5150, and "iPod Shuffle" 5152. In the channel selection panel
5018, the "MP3 Players" node selection 5154 is displayed,
indicating that the treemap shown in the data display panel 5028
represents data just for the "MP3 Players" subnode 5146. Because
the other selection criteria are unchanged, the post count for node
"MP3 Players" 5146 equals the total of the post counts for the
three subnodes 5148, 5150, and 5152 (i.e., 73=42+22+9).
[0075] From the drilled down treemap in FIG. 5L, a user can return
to the previous treemap. In some implementations, a user returns to
the previous treemap by clicking on a button or icon, such as arrow
button 5156 in FIG. 5L. In some implementations, a user can return
to the previous treemap by pressing the ESC key on the keyboard, a
function key such as F7, or a keyboard combination (e.g., switching
from displaying the user interface shown in FIG. 5L back to the
user interface shown in FIG. 5K). The user can return to the top
level treemap in the same way, using a button or icon 5156, or
pressing a designated key or key combination (e.g., switching from
displaying the user interface shown in FIG. 5K back to the user
interface shown in FIG. 5J).
[0076] FIG. 5M illustrates results of drilling down into the
"Engage with HiFi" node 5136. Once this node is selected, the
"Engage with HiFi" selection indicator 5158 is displayed in the
channel selection panel 5018, and the four subnodes "Show us your
Home Theater" 5160, "Cutting Edge Hi-Fi" 5162, "HiFi Ideas" 5164
and "test" 5166 are displayed in the treemap. Because no other
selection criteria have changed, the total post count for the node
"Engage with HiFi" 5136 is the sum of the post counts for the four
subnodes 5160, 5162, 5164, and 5166 (i.e., 723=251+236+145+91). As
shown by the time range sliders 5048 and 5050 (as well as the time
range bar 5052), the data presented in the treemap is for
September, 2010.
[0077] FIGS. 5N-5S illustrate how the display changes as the time
range selection is moved. In each of these figures, a single month
is selected, and the data in the treemap is updated to show the
data for that particular month. Implementations provide various
mechanisms for changing the time range selection. In some
implementations, a user can change the time range selection by
dragging one or both of the time range sliders 5048 and 5050. In
some implementations, a user can modify the time range selection by
clicking and dragging the time selection bar 5052. In some
implementations, the sliders 5048 and 5050 or bar 5052 can be
selected by clicking, then moved by using the left and right cursor
keys on a keyboard.
[0078] In the sequence of FIGS. 5M-5S, a user moves the time range
from September, 2010 to March, 2010 without letting go of the mouse
button. In the illustrated implementation, the numeric data in the
treemap is updated at each step in the process, but the sizes and
shapes of the rectangles in the treemap remain the same. After
reaching March, 2010, the user releases the mouse button, and the
treemap is redrawn, as illustrated in FIG. 5T. In particular, the
"test" subnode has zero posts in March 2010, as shown in FIG. 5S,
so after redrawing the treemap, there is no rectangle for the
"test" subnode as shown in FIG. 5T. In some implementations, the
entire treemap is redrawn at each step in the process, even when
the mouse button has not been released. In some implementations,
nodes with zero or small counts are displayed with a minimal size
so that they remain visible in the user interface shown in FIGS.
5M-5S.
[0079] FIGS. 5U and 5V illustrate how the treemap is updated in the
data panel 5028 when a user switches metrics. In FIG. 5U, a user
has switched to the "page view" metric 5170, which has much higher
numbers (e.g., 58,901 page views for "Cutting edge Hi-Fi") than the
"post count" metrics shown in other Figures (e.g., 236 posts for
"Cutting edge Hi-Fi" for September, 2012, as shown in FIG. 5M). In
FIG. 5V, the user has switched back to the "post count" metric
5168. The contrast of these two metrics shows an interesting point:
whereas "Cutting edge Hi-Fi" has a greater number of page views
than "Show us your Home Theater," more users have made posts to
"Show us your Home Theater." By comparing the data for different
metrics, different time ranges, different users, etc., a user is
enabled to identify important information about an online
community.
[0080] FIGS. 5W-5X describe and illustrate the use of treemaps in
disclosed implementations. A treemap is an effective way to
represent hierarchical data--with each display showing one node and
the subnodes of that node. The relative sizes of the regions in the
treemap identify the relative sizes of the subnodes. In the
examples provided, the regions in each treemap are rectangular, but
this is not required. The regions can have any shape, including
circular cells or voronoi cells. The size of a treemap cell
optionally corresponds to the data for any specified metric (the
same metric applies to all cells in a given treemap). Common
metrics are page views or post counts. In some implementations, the
color of a cell represents the position of the cell in the
hierarchy. For example, in some implementations a cell is yellow if
the cell has no subnodes, and is blue if it does have subnodes. In
some implementations, the color of each cell identifies a category
for the cell, and does not represent position within the hierarchy.
In some implementations, color tone represents a data
characteristic, such as the relative age of the underlying data.
Some implementations also use color saturation to identify a
characteristic of the data.
[0081] FIG. 5W illustrates one example of a treemap for displaying
data for an online community. The hierarchy here is the topic
hierarchy of the online community, the size of each treemap cell
corresponds to the number of posts for that cell, and the different
colors identify different topics. FIG. 5X illustrates using a
treemap to represent stock market data. In this example, the total
market is forms a hierarchy based on industry. At the top level,
the nodes (corresponding to the cells) are "Health Care,"
Financial," "Energy," etc. These general industry categories are
further subdivided, as indicated by the smaller rectangles. Whereas
some implementations of an online community have cell size based on
the number of posts, a treemap for a stock market has cell size
based on market capitalization, or just the number of companies in
each industry. In some implementations of an online community for
the stock market, the size of each cell corresponds to the number
of user posts regarding the companies in each sector. In the
implementation in FIG. 5X, the interface 222 provides configuration
options 5356, which control the display of data. For example, green
hues indicate an increase in stock prices, whereas red hues
indicate decreases in stock prices. At a glance, a user can see
that the health care sector is down, whereas the financial sector
is up. In some implementations, the colors represent consumer
sentiment, with green being positive, red being negative, and black
being neutral. In this scenario, consumers are positive about the
financial sector on this day, but somewhat unhappy about the other
sectors.
[0082] FIG. 6 illustrates examples of a data flow and architecture
supporting the level 1, level 2 and/or level 3 data reporting and
visualization, e.g., in the metric explorers described above. FIG.
6 is merely illustrative, and in actual implementations, more or
fewer components are, optionally, used than those shown. In
addition, some components optionally support a different set of
functions than those described.
[0083] As shown in FIG. 6, application 5602 ("LIA") provides the
platform that supports one or more online communities, e.g., online
communities associated with various business entities (e.g.,
retailers, cosmetic companies, consumer electronics companies,
etc.). The users (e.g., users 102 in FIG. 1) of the online
communities generate content (e.g., blog posts, reviews, comments,
ratings, answers, replies, etc.), and data related to their
interactions (e.g., posting, rating content, registration, replying
to questions, providing comments, etc.) with the communities are
stored in persistent log 5604. Persistent log 5604 is scalable,
fault tolerant, and has built-in redundancy to guard against
failures. Persistent log 5604 stores all the data associated with
the user interaction with the online communities (e.g., community
interaction logs 324, forum data 326 and the like are stored at a
database server 118 that is associated with online community 100 as
shown in FIGS. 1 and 3). The data includes, for example, data
related to the "who", "what", "when", and "where" aspects of the
user interactions with the online communities. In addition,
attributes information (related to the "how" aspect) is also stored
in association with the "what" aspect of the data.
[0084] In some implementations, as shown in FIG. 6, metrics and
attributes are defined by the designer of application 5602, and the
definitions of the metrics and attributes are stored in a
definition store 5606. In general, metrics are raw data concerning
various actions or interactions performed by the users of the
communities. Examples of metrics include page views, posts, kudos,
answers, replies, registrations, comments, logins, etc. Attributes
generally refer to the context information (e.g., device type,
time, community name, topics, keywords, etc.) associated with
particular actions or metrics. Sometimes, attributes also include
environmental variables associated with particular actions or
metrics. In some implementations, the definitions of metrics and
attributes are stored as definition files, and definitions files
can be modified, added, and/or deleted from the definition store
5606.
[0085] In some implementations, as shown in FIG. 6, based on the
definitions of metrics and attributes, some standard aggregation
5608 are defined and stored. Examples of standard aggregations 5608
include total views, average number of posts per day, average
response time, total number of posts, etc. These standard
aggregations are simple computations (e.g., additions,
multiplications, averages, etc.) based on the raw interaction data
recorded for the communities and stored in the persistent logs
5604. The standard aggregations 5608 can be easily defined using
various scripting language, and can be retrieved by queries.
[0086] In some implementations, as shown in FIG. 6, standard
aggregations computed based on actual data recorded on the
communities are stored as standard view data 5610. The standard
view data 5610 can be quickly retrieved and displayed to a viewer
(e.g., a community manager, stakeholder and/or executive) via the
metric explorers described above. As shown in FIG. 6, metric
explorer 5612 (e.g., the user interfaces described above with
reference to FIGS. 5A-5V), and widgets (e.g., the widgets described
with reference to FIGS. 9-207 to 9-236 of U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data") and dashboard 5614 are
supported by a general purpose visualization engine 5616. The
metric explorer 5612, widgets and dashboard 5614, and the general
purpose visualization engine 5616 access the standard view data
5610 directly and present the standard aggregations and raw metrics
to the user, e.g., using the example user interfaces described
earlier. One advantage of computing and storing the standard view
data 5610 is fast response time during interaction with the metric
explorer 5612 and the widget and dashboard 5614, as indicated in
FIG. 6.
[0087] In some implementations, in addition to the standard
aggregations, more complex analytics and metrics (also referred to
as advanced analytics 5618) are defined, as shown in FIG. 6. For
example, advanced analytics algorithms are used to compute various
community health factors (e.g., CHI, FEI, etc.). Other advanced
analytics include metrics for measuring influence of users, hotness
of topics, etc. Typically, these more advanced analytics are not
simple aggregations of raw interactions data recorded on the
communities. Instead, more complex algorithms (e.g., statistical
methods, machine learning methods, non-linear analysis, etc.) are
used to derive these analytics. Typically, these analytics have a
non-linear relationship with the various metrics used to derive the
analytics. In addition, these advanced analytics typically are
based on a large number (e.g., hundreds) of standard metrics.
[0088] In some implementations, the definitions of the advanced
analytics 5618 (also referred to as "derivative definitions") are
stored with the metric definitions and attribute definitions in the
definition store 5606. In some implementations, the advanced
analytics algorithms are implemented using various computing
languages (e.g., matlab, Java, etc.), as shown in FIG. 6. In some
implementations, as shown in FIG. 6, persistent log 5604 and
standard view data 5610 both serve as the data sources for
computing the advanced analytics 5618. In some implementations,
data derivatives 5620 computed based on actual metrics data
according to the advanced analytics algorithms are stored with the
standard view data 5610, for quick retrieval and presentation by
the metric explorer 5612 and widget and dashboard 5614, as shown in
FIG. 6.
[0089] In some implementations, as shown in FIG. 6, an offline data
derivative archive 5622 is implemented to store prior versions of
the data derivatives. In some implementations, the advanced
analytics algorithms are continuously improved and revised
overtime, and the data derivatives 5620 derived using the older
versions of the advanced analytics algorithms are stored in the
offline data derivative archive 5622 with the their respective
version numbers. In some implementations, the data stored in the
offline data derivatives archive 5622 are used to evaluate the
effectiveness of the advanced analytics algorithms, and provide
benchmarking and comparison data for evaluating and validating the
algorithms.
[0090] As described in earlier parts of the specification and shown
in FIG. 6, a customer intelligence center 5624 is used to provide
integrated data service for community managers, and stakeholders
and executives of business entities providing the online
communities. The customer intelligence center 5624 is an integrated
suite of services, providing the metric explorer, and widgets and
dashboard for users to selectively review data associated with
various online communities. As described earlier, the customer
intelligence center 5624 (e.g., as illustrated by the user
interfaces shown in FIGS. 9-150 to 9-161 of U.S. Provisional Patent
Application No. 61/734,927, filed Dec. 7, 2012, entitled "Systems
and Methods for Presenting Analytic Data") not only provides
standard, predefined metrics, and advanced analytics to the users,
but also allows users to define customized metrics and customized
user interfaces (e.g., metrics explorers, widgets and dashboards),
customized reports and visualizations. In addition, the customer
intelligence center 5624 also allows a user (e.g., community
manager, stakeholder, and executive) to establish a notification
system which sends an alert to the user when certain predefined
trigger conditions have been met. For example, a trigger condition
can be a threshold value of a particular metric or data derivative,
and/or a combination of different threshold values for a number of
metrics and data derivatives. The customer intelligence center 5624
access data stored in the standard view data 5610 and data
derivatives 5620 to support its functions.
[0091] Since the customer intelligence center 5624 allows a user to
customize the metrics, aggregations, and data derivatives that can
be reviewed in the customer intelligence center 5624. The
definitions 5626 of the customized metrics, aggregations, and data
derivatives are stored with the standard aggregations 5608. In some
implementations, a metric/attribute to script/query translator 5628
is implemented and used to provide an interface between the
customer intelligence center 5624 and the customized aggregations
5626 and standard aggregations 5608.
[0092] In some implementations, the data calculated according to
the customized metrics, aggregations, and analytics are stored as
custom view data 5630. The custom view data 5630, standard view
data 5610, and data derivatives 5620 (e.g., as stored in database
server(s) 118) together serve as the data source for the reports
and visualizations shown to users via the metric explorer (e.g.,
the user interfaces described above with reference to FIGS. 5A-5V)
and the standard and customized widgets and dashboards (e.g., the
user interfaces described with reference to FIGS. 9-142 to 9-236 of
U.S. Provisional Patent Application No. 61/734,927, filed Dec. 7,
2012, entitled "Systems and Methods for Presenting Analytic Data").
By storing the custom view data 5630, standard view data 5610, and
data derivatives 5620, the responsiveness for the metric explorer
and widgets and dashboards can be improved.
[0093] In some implementations, as shown in FIG. 6, definitions of
the customized metrics, aggregations, widgets, and reports are
stored in the definition store 5606, together with the definitions
of standard metrics, attributes, and data derivatives. In some
implementations, the stored definitions of the customized metrics,
aggregation, widgets, and reports are reused by their creators. In
some implementations, the stored definitions of the customized
metrics, aggregation, widgets, and reports searchable and utilized
by other users who, in some circumstances, will find them useful
for reviewing their own community data in the customer intelligence
center 5624.
[0094] In some implementations, the customer intelligence center
5624 allows a user (e.g., community manager, stakeholder and/or
executives) to establish the criteria for having notifications sent
to a user-specified recipient. In some implementations, a general
purpose notification system 5632 is utilized. A user defines the
trigger event for the notification, the type of information to be
included in the notification, the format of the notification, and a
rate limit for the notification. Examples of trigger events for a
notification include a community health indicator (e.g., CHI) value
having dropped below a threshold value, an average response time
for a particular sub-community having reached a maximum acceptable
value, new registration rate having dropped below a minimum
threshold value, etc. In some implementations, a trigger event can
also be a combination of several conditions. In some
implementations, the user is allowed to specify what information
the notification would include. For example, the user can require
the notification to include actual values of certain metrics and
data derivatives, and/or visualizations and reports of certain
metrics/time periods/channels/users, etc. In some implementations,
the notifications are provided as an email alert, a text message,
or some other kinds of messages. In some implementations, the user
is allowed to specify a preferred rate limit for the notifications.
For example, a rate limit of 2 notifications per day means even if
a trigger event has been met more than twice in a particular day,
only two notifications are sent to the user. In some
implementations, each notification sent to the user can include an
aggregated report related to multiple trigger events that have
occurred in the past but not yet been notified to the user due to
the rate limit. The rate limit allows the user to control the
number of notifications received each month/day/hour, so that the
user is not overwhelmed by too many notification messages.
[0095] As described earlier, the metric explore 5612 is able to
present community data on various metrics filtered by various
attributes. The metrics explorer 5612 obtains the definitions of
the metrics and attributes from the definition store 5606. In
addition to the interaction data recorded on the online communities
supported by the platform application 5602, in some
implementations, the metric explorer also provides data from other
third-party channels 5634, as shown in FIG. 6. For example, data
from third-party channels, such as third-party blogs, micro-blogs
(e.g., twitter), social networks (e.g., Facebook, Google+, etc.),
and other social media (e.g., Youtube, Flickr, etc.), can be
selectively presented in the metric explorer as well. The
integration of social media channels 5634 into the metric explorer
allows users to have a broader view of the impact and health of
their online communities and business objectives.
[0096] In some implementations, as shown in FIG. 6, persistent logs
5604 also store data related to the usage of the customer
intelligence center 5624, such as what customization the user has
made to the metrics explorer, widgets and dashboards, what
customized metrics, aggregations, reports, and widgets have been
created by the customers, what notifications have been implemented
by the customers, how frequently each types of metrics,
aggregations, data derivatives are reviewed by the customers, what
times periods the customers typically wish to review, etc. The data
related to the usage of the customer intelligence center 5624 can
be used to generate additional definitions that are useful for many
other customers. The usage data can also be used to determine what
standard and customer view data to compute and store for easy and
fast access. The usage data is in general useful for improving the
overall efficiency and usability of the customer intelligence
center.
[0097] The foregoing description, for purpose of explanation, has
been described with reference to specific implementations. However,
the illustrative discussions above are not intended to be
exhaustive or to limit the invention to the precise forms
disclosed. Many modifications and variations are possible in view
of the above teachings. The implementations were chosen and
described in order to best explain the principles of the invention
and its practical applications, to thereby enable others skilled in
the art to best utilize the invention and various implementations
with various modifications as are suited to the particular use
contemplated.
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