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.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 62/063,741, titled “Data Visualization” filed Oct. 14, 2014.

BACKGROUND

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

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.

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.

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.

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

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various aspects of the present disclosure. In the drawings:

FIG. 1 illustrates a pipelined architecture in which data flows in a single direction;

FIG. 2 illustrates a block diagram of a system for optimizing the performance of creating and laying out a visualization;

FIG. 3 is a flow chart showing general stages involved in a method for providing data visualization platform performance optimization

FIG. 4 is a block diagram illustrating example physical components of a computing device;

FIGS. 5A and 5B are block diagrams of a mobile computing device; and

FIG. 6 is a block diagram of a distributed computing system.

DETAILED DESCRIPTION

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.

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.

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.

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.

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® 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 1920×1080 pixels has greater resolution than a client 240 with a display resolution of 800×600 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.

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.

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.

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.

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.

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.

When the determination is made to not cull the raw data 105, or when culling is complete, method 300 proceeds to OPERATION 340.

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.).

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.

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.

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.

At OPERATION 370, the computed layout is transferred back to a foreground thread from the background thread.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.