U.S. patent application number 14/683091 was filed with the patent office on 2016-04-14 for performance optimization for data visualization.
This patent application is currently assigned to MICROSOFT TECHNOLOGY LICENSING, LLC.. The applicant listed for this patent is MICROSOFT TECHNOLOGY LICENSING, LLC.. Invention is credited to Barry Christopher Allyn, Michael Woolf.
Application Number | 20160104308 14/683091 |
Document ID | / |
Family ID | 55655563 |
Filed Date | 2016-04-14 |
United States Patent
Application |
20160104308 |
Kind Code |
A1 |
Allyn; Barry Christopher ;
et al. |
April 14, 2016 |
PERFORMANCE OPTIMIZATION FOR DATA VISUALIZATION
Abstract
Performance optimization for reduced and bounded memory cost for
data visualization is provided. Performance optimization comprises:
data culling, geometry culling, and cloning of a visualization to a
background thread for layout. The performance optimization
leverages a data visualization architecture for building of a data
visualization via a one-directional chain of separate stages,
wherein data at each stage may be culled or privatized to reduce
the amount of data, or simplify the nature of the data, to be
processed in subsequent stages, thus improving overall system
performance and user experience.
Inventors: |
Allyn; Barry Christopher;
(Snohomish, WA) ; Woolf; Michael; (Seattle,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MICROSOFT TECHNOLOGY LICENSING, LLC. |
Redmond |
WA |
US |
|
|
Assignee: |
MICROSOFT TECHNOLOGY LICENSING,
LLC.
Redmond
WA
|
Family ID: |
55655563 |
Appl. No.: |
14/683091 |
Filed: |
April 9, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62063741 |
Oct 14, 2014 |
|
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|
Current U.S.
Class: |
345/440 |
Current CPC
Class: |
G06T 2210/44 20130101;
G06T 11/206 20130101; G06T 2200/04 20130101; G06T 1/20 20130101;
G06T 11/40 20130101; G06T 2219/2024 20130101; G06F 16/164 20190101;
G06T 15/40 20130101; G06T 2219/2012 20130101; G06K 9/52 20130101;
G06T 2210/36 20130101; G06T 2219/004 20130101; G06T 13/20 20130101;
G06T 2200/28 20130101; G06T 17/10 20130101; G06T 19/20 20130101;
G06T 13/80 20130101; G06T 11/60 20130101; G06T 11/003 20130101;
G06T 15/08 20130101 |
International
Class: |
G06T 11/20 20060101
G06T011/20; G06T 11/60 20060101 G06T011/60 |
Claims
1. A method for providing performance optimization for data
visualization, comprising: receiving data including raw data,
comprised of a plurality of data points to be displayed via
graphical representations in a visualization, and a surface
description for the visualization; processing the raw data to
determine whether to cull a first data point from the plurality of
data points, wherein the first data point is culled when the
surface description indicates that a graphical representation of
the first data point will be materially affected by a graphical
representation of a second data point; generating abstract geometry
comprised of primitives to graphically represent unculled data
points in the data visualization; processing the abstract geometry
to determine whether to cull abstract geometry, wherein culling the
abstract geometry reduces the primitives comprising the abstract
geometry without materially affecting the visualization to thereby
improve rendering efficiency; and storing the abstract geometry as
a series object within a contiguous block of memory, the series
object configured for near-constant retrieval for the
visualization.
2. The method of claim 1, wherein the steps of the method are
performed in a background thread, such that a client executing the
method does not experience periods of unresponsiveness in a user
interface due to executing the method.
3. The method of claim 1, wherein the surface description further
indicates that the first data point is to be culled when abstract
geometry representing the data points comprising the plurality of
data points contains exceeds an a display area of the
visualization.
4. The method of claim 1, wherein the determination to cull the
first data point is based on a custom culling logic corresponding
to a type of the visualization.
5. The method of claim 1, wherein reducing the primitives
comprising the abstract geometry includes at least one of:
combining collinear primitives; and dropping negligible geometry
from the visualization, such that the dropped negligible geometry
is not stored.
6. The method of claim 1, wherein reducing the primitives
comprising the abstract geometry further comprises: a describing a
master geometry, operable to be stored and retrieved once to
describe several instances; determining, based on the surface
description, whether a data point of the plurality of data points
is an instance of the several instances that will have
corresponding abstract geometry that repeats the master geometry;
and when the data point is an instance, setting the abstract
geometry of the instance to a point in the visualization about
which the master geometry will be built.
7. The method of claim 1, reducing the primitives comprising the
abstract geometry further comprises trimming the primitives
comprising the geometry, wherein the trimming is based on the
surface description.
8. The method of claim 1, wherein the surface description includes
at least one of: a type of the visualization; a size of the
visualization; and a resolution for display of the
visualization.
9. A system for providing performance optimization for data
visualization, comprising: a processor; and a memory storage
including instructions, which when executed by the processor cause
the system to provide: a series layout module, operable to create
abstract geometry comprising primitives to graphically represent
raw data organized according to series in a visualization; and a
layout engine operable to receive a surface description and the raw
data, further comprising: a data culler, operable to process the
raw data to determine whether to cull the raw data based on the
surface description, wherein a first data series of the raw data is
culled when the surface description indicates that a graphical
representation of the first data series will be materially affected
by a graphical representation of a second data series of the raw
data; and a geometry culler, operable to process the abstract
geometry to determine whether the abstract geometry can be culled,
wherein culling the abstract geometry reduces the primitives
comprising the abstract geometry without materially affecting the
visualization to thereby improve rendering efficiency.
10. The system of claim 9, wherein the layout engine is operable to
receive the raw data and the surface description from a client
operable to render the visualization, and to transmit the
visualization to the client.
11. The system of claim 9, wherein the abstract geometry is stored
as series objects within continuous blocks of memory, wherein each
series object corresponds to one data series and is configured for
near-constant retrieval.
12. The system of claim 9, wherein the determination to cull the
first data point is based on a custom culling logic corresponding
to a type of the visualization.
13. The system of claim 9, wherein the determination to cull the
abstract geometry is based on a geometry culling logic that
indicates the abstract geometry to cull based on a size threshold
for the abstract geometry, wherein the size threshold is based on
the surface description.
14. The system of claim 9, wherein the data culler is further
operable to, based on the surface description, cull the first data
series when a number of data series comprising the raw data exceeds
a display area of the visualization.
15. The system of claim 9, wherein the geometry culler is operable
to combine collinear primitives to reduce the primitives.
16. The system of claim 9, wherein the geometry culler is operable
to drop negligible geometry from the visualization to reduce the
primitives.
17. The system of claim 9, wherein the geometry culler is to
operable trim the primitives comprising the abstract geometry to
reduce the primitives, wherein the trimming is based on the surface
description.
18. A computing device for providing performance optimization for
data visualization comprising: a processor; and a memory storage
including instructions, which when executed by the processor cause
the computing device to be operable to: receive data including raw
data, comprised of a plurality of data points to be displayed via
graphical representations in a visualization, and a surface
description for the visualization; process the raw data based on a
custom culling logic corresponding to a type of the visualization
to determine whether to cull a first data point from the plurality
of data points, wherein the first data point is culled when the
surface description indicates that a graphical representation of
the first data point will be materially affected by a graphical
representation of a second data point; generate abstract geometry
comprised of primitives to graphically represent unculled data
points in the data visualization; process the abstract geometry
culling logic to determine whether to cull abstract geometry based
on a size threshold for the abstract geometry based on the surface
description, wherein culling the abstract geometry reduces the
primitives comprising the abstract geometry without materially
affecting the visualization to thereby improve rendering
efficiency; and store the abstract geometry as a series object
within a continuous block of memory, the series object configured
for near-constant retrieval for the visualization.
19. The computing device of claim 18, wherein the series object
corresponds to a data series of the raw data, wherein the abstract
geometry for each data series is generated, processed, and stored
individually.
20. The computing device of claim 18, wherein reducing the
primitives includes at least one of: combining collinear
primitives; dropping negligible geometry from the visualization,
such that the dropped negligible geometry is not stored; and
trimming the primitives comprising the geometry, wherein the
trimming is based on the surface description.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 62/063,741, titled "Data Visualization"
filed Oct. 14, 2014.
BACKGROUND
[0002] Data visualization is a process for graphically representing
data in a visualization, for example, a chart, an infographic, a
map, a gauge, etc. Visualizations of large data sets require
signification system resources, including processor time and
memory, to prepare or store the visualization, which can cause the
system to lock up or slow down. It is with respect to these and
other considerations that examples will be made.
SUMMARY
[0003] This summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description section. This summary is not intended to
identify all features of the claimed subject matter, nor is it
intended to limit the scope of the claimed subject matter.
[0004] Aspects of the present disclosure provide performance
optimization by culling data from a data visualization to reduce
memory requirements. According to one aspect, data is culled during
layout time to intelligently skip data that does not materially
impact the presentation of the visualization; preserving the
presentation while reducing complexity. According to another
aspect, geometry produced during layout is culled such that the
geometry vectors are reduced or simplified/trimmed to reduce
post-layout processing (e.g., rendering). According to another
aspect, each series layout uses private optimized data structures
to store geometry in abstract form for reduced memory usage.
Aspects of the present disclosure also provide for deferring the
cost of layout to a background thread by cloning a visualization
and performing layout on the background thread, then transferring
the computed layout to the foreground thread in near constant
time.
[0005] Examples may be implemented as a computer process, a
computing system, or as an article of manufacture such as a
computer program product or computer readable media. The computer
program product may be a computer storage media readable by a
computer system and encoding a computer program of instructions for
executing a computer process.
[0006] The details of one or more aspects are set forth in the
accompanying drawings and description below. Other features and
advantages will be apparent from a reading of the following
detailed description and a review of the associated drawings. It is
to be understood that the following detailed description is
explanatory only and is not restrictive of the present
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various aspects of
the present disclosure. In the drawings:
[0008] FIG. 1 illustrates a pipelined architecture in which data
flows in a single direction;
[0009] FIG. 2 illustrates a block diagram of a system for
optimizing the performance of creating and laying out a
visualization;
[0010] FIG. 3 is a flow chart showing general stages involved in a
method for providing data visualization platform performance
optimization
[0011] FIG. 4 is a block diagram illustrating example physical
components of a computing device;
[0012] FIGS. 5A and 5B are block diagrams of a mobile computing
device; and
[0013] FIG. 6 is a block diagram of a distributed computing
system.
DETAILED DESCRIPTION
[0014] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar elements. While aspects of the
disclosure may be described, modifications, adaptations, and other
implementations are possible. For example, substitutions,
additions, or modifications may be made to the elements illustrated
in the drawings, and the methods described herein may be modified
by substituting, reordering, or adding stages to the disclosed
methods. Accordingly, the following detailed description does not
limit the present disclosure, but instead, the proper scope of the
disclosure is defined by the appended claims. Examples may take the
form of a hardware implementation, or an entirely software
implementation, or an implementation combining software and
hardware aspects. The following detailed description is, therefore,
not to be taken in a limiting sense.
[0015] As is commonly known in the art, memory is often a
bottleneck for application performance. Aspects allow for
application performance to be optimized by bounding the amount of
memory used and by storing data in a single, contiguous allocation.
As described below, when geometry is computed by a layout engine,
it may be cached within a bounded series object. Aspects provide
for data culling and the privatization, on a per-layout basis, for
storing abstracting geometry to optimize performance.
[0016] Examples of the present disclosure are directed to providing
performance optimization within a data visualization platform
architecture via culling data from a data visualization. According
to an aspect, the architecture enables building of a data
visualization (e.g., a chart, an infographic, a map, a gauge, etc.)
via a one-directional chain of separate stages, each stage having a
simple input interface and output interface.
[0017] FIG. 1 illustrates a pipelined architecture 100 in which
data flows in a single direction. As illustrated in FIG. 1, data
flows from raw data 105, to abstract geometry 115, to series object
125, to visualization 135. Data can be privatized or culled at each
stage in the pipeline, which reduces the memory used for laying out
and creating a visualization 135. Accordingly, visualization
generation is performed more efficiently.
[0018] Raw data 105 comprises the collection of data points to be
plotted in a visualization 135. Raw data 105 in various aspects is
organized by rows, vectors, arrays, tables, matrices, etc. In on
example, raw data 105 is taken from a group of cells in the
EXCEL.RTM. spreadsheet software, offered by MICROSOFT CORPORATION
of Redmond, Wash. Visualizations 135 include, for example, charts,
infographics, maps, gauges, etc., which are used to graphically
display the raw data 105.
[0019] Abstract geometry 115 comprises a limited set of primitives
(e.g., lines, Beziers curves, Bezier surfaces, etc.) which can be
passed directly to an appropriate rendering Application Programming
Interface (API) or to an additional module or engine for further
processing. From these primitives, any geometry can be
approximated.
[0020] According to aspects, the abstract geometries 115 are stored
as a series object 125 in a compact form that is tailored to each
type of layout. In several aspects, the abstract geometries 115 are
assembled into a series object 125 that is stored in a single
allocation as a continuous block in memory, which improves the
speed of retrieval. According to aspects, a series object 125 is a
form of privatized storage, which is operable to provide all the
abstract geometries 115 comprising it without being interrogated
for individual abstract geometries 115; the entire series object
125 is provided in one synchronous operation to produce the
visualization 135. According to aspects, the amount of abstract
geometry 115 cached within a series object 125 is bounded by the
display size of the visualization 135 and is computed to have a
fixed cost in memory. When the size of series objects 125 is
bounded, some aspects use multiple series objects 125 to create
portions of the visualization 135. One example of a series objects
125 is a circle comprised of abstract geometry 115 (e.g., four
cubic Beziers, each comprising a quadrant of the circle), which may
represent the raw data 105 in the visualization 135 as a function
of the circle's radius.
[0021] Abstract geometries 115 are stored in various forms
according to various aspects. According to one aspect, abstract
geometries 115 are stored as a master and instances compact form
(e.g., lists of rectangles, circles, diamonds, lines, pie slices,
etc.). A master and instances compact form enables a visualization
type that uses geometry with repeating forms (e.g., a scatter
series where each data point is a diamond shape) to improve
performance by compacting the volume of series objects 125 to be
provided. Aspects enable the visualization 135 to use geometry in
the master/instances form, whereby the master geometry of a series
object 125 (e.g., a diamond) is described once in full detail and
the instances reference the master geometry and are described as a
single point (e.g., the center of the diamond) about which the
master geometry is constructed in the visualization 135. According
to another aspect, abstract geometries 115 can be stored as path
geometry (e.g., area charts, surface charts, radar charts, trend
lines, etc.). According to another aspect, abstract geometries 115
can be stored as a formula. For example, in a business chart
plotting supply and demand curves, functions describing the curves
are stored. Accordingly, the abstract geometry 115 can be
synthesized during rendering. For example, in cases of simple
layout (e.g., line charts, column charts, etc.) that are
computationally inexpensive and where the data is local, abstract
geometry 115 may be synthesized directly from the raw data 105.
[0022] FIG. 2 illustrates a block diagram of a system 200 for
optimizing the performance of creating and laying out a
visualization 135. In the system 200, data is passed to a layout
engine 210 from client 240 during the layout phase of creating a
visualization 135, processed, and abstract geometry 115 is passed
back to the client 240 to provide the visualization 135. The data
received from the client 240 includes raw data 105 and a surface
description 235, which provides context on the client 240 and a
coordinate space in which the raw data 105 will be visualized.
According to one aspect, data is passed to a series layout module
250 to create abstract geometry 115 according to the surface
description 235 to graphically represent the raw data 105 in a
visualization 135, which in turn is passed back to the layout
engine 210 to cull the abstract geometry 115 before it is
transmitted to the client 240. The system 200 is operable to
privatize or cull data or geometry at any point.
[0023] During the layout phase of creating a visualization 135, raw
data 105 received from the client 240 is converted into abstract
geometry 115. According to an aspect, when the layout engine 210 is
constructing a layout, the data culler 220 performs a
layout-specific culling of the raw data 105 using custom culling
logic. Raw data 105 that, if removed, does not materially impact
the visualization 135, as determined by the custom culling logic,
is culled; it is ignored or skipped when geometry is calculated.
According to an aspect, data that is culled is retained by the
layout engine 210 or the client 240, but is not transmitted to the
series layout module 250 or used in subsequent operations. The data
culler 220 enables the layout engine 210 to construct a
visualization 135 that will still convey the same interpretation of
the raw data 105, but using less data.
[0024] According to aspects, raw data 105 is culled when its
graphical representation in the visualization 135 is materially
affected by the presentation of other raw data 105. For example, in
a visualization 135 of a column series, where data series
comprising the raw data 105 are visualized as vertical columns,
data series are culled from the raw data 105 when the vertical
columns of other data series would overlap them in the
visualization 135. In another example, in a visualization 135 of a
bubble series, raw data 105 (represented as circles) are culled
from areas of high density within the bubble chart.
[0025] According to aspects, each visualization type comprises
custom culling logic appropriate for its layout. In these aspects,
a particular culling logic is chosen based on the visualization
type (e.g., column, scatter, pie, etc.) that selectively
skips/ignores raw data 105 that would produce abstract geometry 115
or series objects 125 that materially affect the display of other
abstract geometry 115 or series objects 125. According to aspects,
the raw data 105 is not deleted in a cull; it is merely ignored for
purposes of creating a visualization 135. In various aspects, raw
data 105 that is not culled is converted to the appropriate
primitives that can be used to synthesize geometry for downstream
processes in the pipelined architecture 100 (e.g., rendered or
interacted with via the visualization 135). By culling the raw data
105, processes occurring later in the pipelined architecture 100
are provided with a reduced amount of data to manipulate while
providing an equivalent interpretation of the data.
[0026] According to another aspect, as the abstract geometry 115 is
produced during the layout phase, a geometry culler 230 culls
abstract geometry 115 further to reduce rendering and rasterization
costs of abstract geometry 115 and series objects 125 that are too
complex for the current output resolution of the client 240 (or the
device on which the client 240 is executed). In various aspects,
the geometry culler 230 executes geometry culling logic to drop
abstract geometry 115 or convert it to a simpler form when the
visualized abstract geometry 115 will fall below a size threshold
within the visualization 135. According to an aspect, the geometry
culler 230 is operable to drop abstract geometry 115 when the
culling will not materially impact the displayed visualization 135.
For example, the geometry culler 230 drops the abstract geometry
115 for empty series objects 125 and line segments that will be
rendered in the visualization 135 with zero-length, trims/converts
rectangles with zero height/width and short Bezier curves (e.g.,
less than 4 pixels) into lines, combines collinear segments, etc.
According to aspects, the geometry culler 230 reduces the number of
primitives needed to display a set of abstract geometry 115 without
materially affecting the visualization 135 according to the surface
description 235. By presenting abstract geometry 115 comprised of
fewer or simpler primitives (e.g., lines instead of Beziers),
geometry culling reduces the amount of processing required by
subsequent stages in the pipelined architecture 100.
[0027] Aspects provide for a surface description 235 (e.g.,
visualization type, visualization size, client resolution/dpi,
etc.) to be generated for the visualization 135 to provide client
context on which the culling thresholds are based. For example, a
client 240 with a display resolution of 1920.times.1080 pixels has
greater resolution than a client 240 with a display resolution of
800.times.600 pixels, which is not able to display the same
visualization 135 with as great of detail as the client 240 with
the larger resolution. Accordingly, a geometry for a rectangle
displayed on the client 240 with the larger resolution may be
culled to be displayed as a line (or not displayed at all) on the
client 240 with the smaller resolution. The surface description 235
is used by the data culler 220 to determine when abstract
geometries 115 will materially impact one another (and thereby cull
the associated raw data 105) and by the geometry culler 230 to
determine when an abstract geometry 115 can be dropped or
simplified/trimmed without materially affecting the display of the
visualization 135.
[0028] According to aspects, the entire data set is processed
during the layout phase in order to produce the correct and reduced
set of abstract geometries 115, can take a long time and can
introduce brief hangs and moments of unresponsiveness in the User
Interface (UI) of a client 240. For example, to create a
visualization 135 based on a million rows of raw data 105, a
million rows of raw data 105 are "walked through" (i.e., processed
row-by-row) to perform the data culling process and the resulting
abstract geometries 115 are similarly processed to perform the
geometry culling. Aspects provide for deferring the cost of the
layout phase to a background thread to improve responsiveness by
allowing the client 240 to clone the visualization 135 and push the
layout phase to a background thread. As is known in the art,
cloning can be achieved in near-constant time (e.g., less than 0.5
ms). The background layout process allows the client 240 to still
be responsive to user input while the layout of the visualization
135 is calculated in the background. Aspects allow for the
foreground visualization 135 to remain blank, display a previous
layout, display a progress bar (or similar indication of the
ongoing layout process) for the background thread or combinations
thereof. Aspects also allow for the background thread to be aborted
by the client 240, such as, for example, when a user manually
aborts or when a second request is made. According to aspects, once
the background layout phase is complete, the computed layout can be
transferred back to a foreground thread at near constant time via
an API that involves a pointer swap to replace or update the
visualization 135 in the foreground.
[0029] FIG. 3 is a flow chart showing general stages involved in a
method 300 for providing data visualization platform performance
optimization. Method 300 begins at START 301 and proceeds to
OPERATION 310, where the data to be used in a visualization 135 is
received. According to aspects, the received data includes raw data
105 and the surface description 235 for the visualization 135.
[0030] Method 300 then proceeds to OPERATION 320, where the layout
is pushed to a background thread. The layout is pushed to a
background thread to prevent hangs or moments of unresponsiveness
that may be introduced in the UI of a client 240 during processing.
Method 300 then proceeds to DECISION OPERATION 330.
[0031] At DECISION OPERATION 330, a determination is made as to
whether to cull the raw data 105 based on the coordinate system
requirements (e.g., Cartesian, value/value (e.g. scatter chart);
Cartesian, category/value (e.g., column chart); radial,
category/value (e.g., pie chart, radar chart); etc.) and display
dimensions for the visualization 135 retrieved via the surface
description 235. From the surface description 235 and an analysis
of the raw data 105, a determination can be made whether the
geometry for two data series or data points will overlap on the
display surface. For example, when creating a five-inch-wide column
chart on a monitor that has a hundred pixels per inch (i.e., a
chart of 500 pixels), a determination can be made that the
visualization can draw at most 500 one-pixel-wide columns in the
example chart. Accordingly, displaying more than 500 data series of
the raw data 105 would cause the associated abstract geometry 115
to materially affect one another (e.g., overlap), and it can be
determined that the raw data 105 is to be culled.
[0032] When the determination is made to cull the raw data 105,
method 300 proceeds to OPERATION 335, where, according to aspects,
the raw data 105 is culled according to layout-specific, custom
culling logic (e.g., for a scatter series, overlapping markers are
dropped) and method 300 then proceeds to OPERATION 340. According
to various aspects, raw data 105 may be culled according to several
culling schemes, which may be user-defined or set by the system
based on the visualization type, according to various aspects,
including: by sequential truncation (e.g., ignoring data series
after a threshold is reached), interleaved truncation (e.g., every
other data series is culled), merging (e.g., combining small data
series in appropriate visualization, such as a pie chart, into a
"miscellaneous" data series), outlier culling, etc.
[0033] When the determination is made to not cull the raw data 105,
or when culling is complete, method 300 proceeds to OPERATION
340.
[0034] Abstract geometries 115 are calculated and generated at
OPERATION 340 to represent the raw data 105 in the visualization
135. According to an aspect, abstract geometries 115 are calculated
and generated individually for each data series comprising the raw
data 105. According to an aspect, abstract geometries are comprised
of primitives (e.g., lines, Bezier curves, Bezier surfaces,
etc.).
[0035] Method 300 then proceeds to DECISION OPERATION 350, where is
it determined whether to cull the abstract geometry 115 based on
the display characteristics of the client 240 and the visualization
135 retrieved via the surface description 235. Continuing the
example given in relation to DECISION OPERATION 330, if 500 columns
were to be rendered as rectangles having a width of one pixel, the
decision to cull (via trimming) their geometry from rectangles to
lines can be made without materially affecting the visualization
135; the visualization 135 will look substantially the same to a
user.
[0036] When the determination is made to cull the abstract geometry
115, method 300 proceeds to OPERATION 355, where the abstract
geometry 115 is culled according to geometry culling logic. In
aspects, culling the primitives comprising the abstract geometry
115 includes, but is not limited to: setting a master and instance
format, so that a geometry is only passed once to the client 240;
trimming the primitives of abstract geometries (e.g., a rectangle
of width/height of one pixel can be represented as a line, a short
curve can be represented as a line, etc.), to reduce the amount of
processing needed by the client 240; dropping negligible geometry,
such as the abstract geometry 115 corresponding to data series that
are empty, zero-value, or too small to be accurately displayed in
the visualization 135 (e.g., slices of a pie chart that would be
too thin to be accurately displayed as a line on the chart) to
reduce rendering time needed by the client 240; combining the
primitives of collinear segments to group processes for the client;
etc. According to aspects, because abstract geometry 115 is
generated individually for each data series, it is also processed
and culled individually, which allows method 300 to begin geometry
culling before all abstract geometry 115 has been generated at
OPERATION 340.
[0037] When the determination is made to not cull the abstract
geometry, or when culling is complete, method 300 proceeds to
OPERATION 360, where the abstract geometries 115 are privately
stored on a per-series layout basis, such as, for example, via a
series object 125. In various aspects, series objects 125 are
stored in a single allocation as a contiguous block in memory,
which improves the speed of retrieval. In aspects, a series object
125 is bounded by the display constraints of the visualization 135,
as indicated by the surface description 235, and is computed to
have a fixed cost in memory, such that it can be retrieved from
memory in near-constant time (e.g., less than 0.5 ms). In some
aspects, each series object 125 corresponds to a data series (or,
in other aspects, a combined data series) and can be stored
individually, which allows method 300 to begin storing before all
abstract geometry 115 has been culled according to OPERATION 355.
According to an aspect, several series objects 125 are stored in
adjacent continuous blocks in memory, which further improves the
speed of retrieval in OPERATION 380. Method 300 then proceeds to
OPERATION 370.
[0038] At OPERATION 370, the computed layout is transferred back to
a foreground thread from the background thread.
[0039] Method 300 then proceeds to OPERATION 380, where the
abstract geometry 115 is provided to the client 240. According to
aspects, the abstract geometry 115 is streamed as series objects
125 to the client 240. After the client 240 receives the abstract
geometry 115, the visualization 135 can be provided, and method 300
concludes at END 399.
[0040] FIGS. 4-6 and the associated descriptions provide a
discussion of a variety of operating environments in which examples
of the disclosure may be practiced. However, the devices and
systems illustrated and discussed with respect to FIGS. 4-6 are for
purposes of example and illustration and are not limiting of a vast
number of computing device configurations that may be used for
practicing aspects of the disclosure, described herein.
[0041] FIG. 4 is a block diagram illustrating physical components
(i.e., hardware) of a computing device 400 with which examples of
the present disclosure may be practiced. The computing device
components described below may be suitable for the client device
described above. In a basic configuration, the computing device 400
may include at least one processing unit 402 and a system memory
404. Depending on the configuration and type of computing device,
the system memory 404 may comprise, but is not limited to, volatile
storage (e.g., random access memory), non-volatile storage (e.g.,
read-only memory), flash memory, or any combination of such
memories. The system memory 404 may include an operating system 405
and one or more programming modules 406 suitable for running
software applications 450, such as layout engine 210. According to
an aspect, the system memory 404 may include the client 240. The
operating system 405, for example, may be suitable for controlling
the operation of the computing device 400. Furthermore, aspects of
the invention may be practiced in conjunction with a graphics
library, other operating systems, or any other application program
and is not limited to any particular application or system. This
basic configuration is illustrated in FIG. 4 by those components
within a dashed line 408. The computing device 400 may have
additional features or functionality. For example, the computing
device 400 may also include additional data storage devices
(removable or non-removable) such as, for example, magnetic disks,
optical disks, or tape. Such additional storage is illustrated in
FIG. 4 by a removable storage device 409 and a non-removable
storage device 410.
[0042] As stated above, a number of program modules and data files
may be stored in the system memory 404. While executing on the
processing unit 402, the program modules 406 (e.g., client 240,
layout engine 210) may perform processes including, but not limited
to, one or more of the stages of the method 300 illustrated in FIG.
3. Other program modules that may be used in accordance with
examples of the present disclosure and may include other
applications 450 such as, for example, electronic mail and contacts
applications, word processing applications, spreadsheet
applications, database applications, slide presentation
applications, drawing or computer-aided application programs,
etc.
[0043] Furthermore, examples of the disclosure may be practiced in
an electrical circuit comprising discrete electronic elements,
packaged or integrated electronic chips containing logic gates, a
circuit using a microprocessor, or on a single chip containing
electronic elements or microprocessors. For example, examples of
the disclosure may be practiced via a system-on-a-chip (SOC) where
each or many of the components illustrated in FIG. 4 may be
integrated onto a single integrated circuit. Such an SOC device may
include one or more processing units, graphics units,
communications units, system virtualization units and various
application functionality all of which are integrated (or "burned")
onto the chip substrate as a single integrated circuit. When
operating via an SOC, the functionality, described herein, may be
operated via application-specific logic integrated with other
components of the computing device 400 on the single integrated
circuit (chip). Examples of the present disclosure may also be
practiced using other technologies capable of performing logical
operations such as, for example, AND, OR, and NOT, including but
not limited to mechanical, optical, fluidic, and quantum
technologies. In addition, aspects of the disclosure may be
practiced within a general purpose computer or in any other
circuits or systems.
[0044] The computing device 400 may also have one or more input
device(s) 412 such as a keyboard, a mouse, a pen, a sound input
device, a touch input device, etc. The output device(s) 414 such as
a display, speakers, a printer, etc. may also be included. The
aforementioned devices are examples and others may be used. The
computing device 400 may include one or more communication
connections 416 allowing communications with other computing
devices 418. Examples of suitable communication connections 416
include, but are not limited to, RF transmitter, receiver, or
transceiver circuitry; universal serial bus (USB), parallel, or
serial ports.
[0045] The term computer readable media as used herein may include
computer storage media. Computer storage media may include volatile
and nonvolatile, removable and non-removable media implemented in
any method or technology for storage of information, such as
computer readable instructions, data structures, or program
modules. The system memory 404, the removable storage device 409,
and the non-removable storage device 410 are all computer storage
media examples (i.e., memory storage.) Computer storage media may
include RAM, ROM, electrically erasable programmable read-only
memory (EEPROM), flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices, or any other article of manufacture which can be
used to store information and which can be accessed by the
computing device 400. Any such computer storage media may be part
of the computing device 400. Computer storage media does not
include a carrier wave or other propagated data signal.
[0046] Communication media may be embodied by computer readable
instructions, data structures, program modules, or other data in a
modulated data signal, such as a carrier wave or other transport
mechanism, and includes any information delivery media. The term
"modulated data signal" may describe a signal that has one or more
characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media may include wired media such as a wired network
or direct-wired connection, and wireless media such as acoustic,
radio frequency (RF), infrared, and other wireless media.
[0047] FIGS. 5A and 5B illustrate a mobile computing device 500,
for example, a mobile telephone, a smart phone, a tablet personal
computer, a laptop computer, and the like, with which aspects of
the disclosure may be practiced. With reference to FIG. 5A, an
example of a mobile computing device 500 for implementing the
aspects is illustrated. In a basic configuration, the mobile
computing device 500 is a handheld computer having both input
elements and output elements. The mobile computing device 500
typically includes a display 505 and one or more input buttons 510
that allow the user to enter information into the mobile computing
device 500. The display 505 of the mobile computing device 500 may
also function as an input device (e.g., a touch screen display). If
included, an optional side input element 515 allows further user
input. The side input element 515 may be a rotary switch, a button,
or any other type of manual input element. In alternative examples,
mobile computing device 500 may incorporate more or less input
elements. For example, the display 505 may not be a touch screen in
some examples. In alternative examples, the mobile computing device
500 is a portable phone system, such as a cellular phone. The
mobile computing device 500 may also include an optional keypad
535. Optional keypad 535 may be a physical keypad or a "soft"
keypad generated on the touch screen display. In various aspects,
the output elements include the display 505 for showing a graphical
user interface (GUI), a visual indicator 520 (e.g., a light
emitting diode), or an audio transducer 525 (e.g., a speaker). In
some examples, the mobile computing device 500 incorporates a
vibration transducer for providing the user with tactile feedback.
In yet another example, the mobile computing device 500
incorporates peripheral device ports 540, such as an audio input
(e.g., a microphone jack), an audio output (e.g., a headphone
jack), and a video output (e.g., a HDMI port) for sending signals
to or receiving signals from an external device.
[0048] FIG. 5B is a block diagram illustrating the architecture of
one example of a mobile computing device. That is, the mobile
computing device 500 can incorporate a system (i.e., an
architecture) 502 to implement some examples. In one example, the
system 502 is implemented as a "smart phone" capable of running one
or more applications (e.g., browser, e-mail, calendaring, contact
managers, messaging clients, games, and media clients/players). In
some examples, the system 502 is integrated as a computing device,
such as an integrated personal digital assistant (PDA) and wireless
phone.
[0049] One or more application programs 450, for example, client
240, may be loaded into the memory 562 and run on or in association
with the operating system 564. Examples of the applications 450
include phone dialer programs, e-mail programs, personal
information management (PIM) programs, word processing programs,
spreadsheet programs, Internet browser programs, messaging
programs, and so forth. According to an aspect, the layout engine
210 may be loaded into memory 562. The system 502 also includes a
non-volatile storage area 568 within the memory 562. The
non-volatile storage area 568 may be used to store persistent
information that should not be lost if the system 502 is powered
down. The applications 450 may use and store information in the
non-volatile storage area 568, such as e-mail or other messages
used by an e-mail application, and the like. A synchronization
application (not shown) also resides on the system 502 and is
programmed to interact with a corresponding synchronization
application resident on a host computer to keep the information
stored in the non-volatile storage area 568 synchronized with
corresponding information stored at the host computer. As should be
appreciated, other applications may be loaded into the memory 562
and run on the mobile computing device 500.
[0050] The system 502 has a power supply 570, which may be
implemented as one or more batteries. The power supply 570 might
further include an external power source, such as an AC adapter or
a powered docking cradle that supplements or recharges the
batteries.
[0051] The system 502 may also include a radio 572 that performs
the function of transmitting and receiving radio frequency
communications. The radio 572 facilitates wireless connectivity
between the system 502 and the "outside world," via a
communications carrier or service provider. Transmissions to and
from the radio 572 are conducted under control of the operating
system 564. In other words, communications received by the radio
572 may be disseminated to the application programs 450 via the
operating system 564, and vice versa.
[0052] The visual indicator 520 may be used to provide visual
notifications or an audio interface 574 may be used for producing
audible notifications via the audio transducer 525. In the
illustrated example, the visual indicator 520 is a light emitting
diode (LED) and the audio transducer 525 is a speaker. These
devices may be directly coupled to the power supply 570 so that
when activated, they remain on for a duration dictated by the
notification mechanism even though the processor 560 and other
components might shut down for conserving battery power. The LED
may be programmed to remain on indefinitely until the user takes
action to indicate the powered-on status of the device. The audio
interface 574 is used to provide audible signals to and receive
audible signals from the user. For example, in addition to being
coupled to the audio transducer 525, the audio interface 574 may
also be coupled to a microphone to receive audible input, such as
to facilitate a telephone conversation. The system 502 may further
include a video interface 576 that enables an operation of an
on-board camera 530 to record still images, video stream, and the
like.
[0053] A mobile computing device 500 implementing the system 502
may have additional features or functionality. For example, the
mobile computing device 500 may also include additional data
storage devices (removable or non-removable) such as, magnetic
disks, optical disks, or tape. Such additional storage is
illustrated in FIG. 5B by the non-volatile storage area 568.
[0054] Data/information generated or captured by the mobile
computing device 500 and stored via the system 502 may be stored
locally on the mobile computing device 500, as described above, or
the data may be stored on any number of storage media that may be
accessed by the device via the radio 572 or via a wired connection
between the mobile computing device 500 and a separate computing
device associated with the mobile computing device 500, for
example, a server computer in a distributed computing network, such
as the Internet. As should be appreciated such data/information may
be accessed via the mobile computing device 500 via the radio 572
or via a distributed computing network. Similarly, such
data/information may be readily transferred between computing
devices for storage and use according to well-known
data/information transfer and storage means, including electronic
mail and collaborative data/information sharing systems.
[0055] FIG. 6 illustrates one example of the architecture of a
system for providing data visualization as described above. Content
developed, interacted with, or edited in association with the
client 240 or the layout engine 210 may be stored in different
communication channels or other storage types. For example, various
documents may be stored using a directory service 622, a web portal
624, a mailbox service 626, an instant messaging store 628, or a
social networking site 630. The client 240 or layout engine 210 may
use any of these types of systems or the like for providing data
visualization, as described herein. A server 615 may provide the
client 240 or layout engine 210 to clients 605A-C. As one example,
the server 615 may be a web server providing the client 240 or
layout engine 210 over the web. The server 615 may provide the
client 240 or layout engine 210 over the web to clients 605 through
a network 610. By way of example, the client computing device may
be implemented and embodied in a personal computer 605A, a tablet
computing device 605B or a mobile computing device 605C (e.g., a
smart phone), or other computing device. Any of these examples of
the client computing device may obtain content from the store
616.
[0056] Aspects of the present disclosure, for example, are
described above with reference to block diagrams or operational
illustrations of methods, systems, and computer program products
according to aspects of the disclosure. The functions/acts noted in
the blocks may occur out of the order as shown in any flowchart.
For example, two blocks shown in succession may in fact be executed
substantially concurrently or the blocks may sometimes be executed
in the reverse order, depending upon the functionality/acts
involved.
[0057] The description and illustration of one or more examples
provided in this application are not intended to limit or restrict
the scope of the present disclosure in any way. The aspects,
examples, and details provided in this application are considered
sufficient to convey possession and enable others to make and use
the best mode of present disclosure. The present disclosure should
not be construed as being limited to any aspect, example, or detail
provided in this application. Regardless of whether shown and
described in combination or separately, the various features (both
structural and methodological) are intended to be selectively
included or omitted to produce an example with a particular set of
features. Having been provided with the description and
illustration of the present application, one skilled in the art may
envision variations, modifications, and alternate examples falling
within the spirit of the broader aspects of the general inventive
concept embodied in this application that do not depart from the
broader scope of the present disclosure.
* * * * *