The invention relates generally to big data displays and more particularly to systems and methods to provide visualization of big data.
An enterprise may be able to access substantial amounts of data. For example, an enterprise operating several businesses may constantly be updating financial information (e.g., sales, profits, outstanding purchase orders, etc.). It can be difficult, however, for a person to look at the data and understand what the information means (e.g., a person looking at tens of thousands parameter values may find it difficult to identify trends or correlations within the data). Moreover, client platforms, such as personal computers executing browsers, smartphone applications, etc. may not typically present large quantities of data in an understandable format. For example, a spreadsheet containing columns of numbers may make it difficult for a manager or Information Technology (“IT”) specialist to make comparisons, especially when there are a large number of businesses and/or parameters to be considered. It would therefore be desirable to facilitate a visualization of big data in such a way so as to improve a person's ability to interpret the big data efficiently and/or accurately.
Some embodiments are associated with a big data pull infrastructure adapted to provide a substantial number of electronic files, originating from a plurality of data sources, to be ingested and validated. A visualization system may collect meta information associated with the electronic files received from the big data pull infrastructure. According to some embodiments, a hierarchical, multidimensional view of the meta data associated with the electronic files may be established. Moreover, the hierarchical, multidimensional view of the meta data may be rendered by the visualization system as nested icons, at least one icon being represented via a plurality of unique visual characteristics indicating: (i) data that has not been ingested, (ii) data that has been ingested but not yet validated, and (iii) data that has been ingested and validated.
Other embodiments may be associated with a big data pull infrastructure adapted to provide a substantial number of electronic files, originating from a plurality of data sources. A visualization system may collect meta information associated with the electronic files received from the big data pull infrastructure. According to some embodiments, a data flow view is rendered graphically indicating flows of information from data sources to data destinations. Moreover, a data exploration view may be rendered to graphically indicate a plurality of category icons, each icon representing a different type of data category, wherein nested sub-category icons are displayed within each category icon.
Other embodiments are associated with systems and/or computer-readable medium storing instructions to perform any of the methods described herein.
Some embodiments disclosed herein facilitate a visualization of big data in such a way so as to improve a person's ability to interpret the big data efficiently and/or accurately. Some embodiments are associated with systems and/or computer-readable medium that may help perform such a method.
Reference will now be made in detail to present embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description have been used to refer to like or similar parts of the invention.
Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment may be used on another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
Some embodiments described herein may automatically facilitate a visualization of big data in such a way so as to improve a person's ability to interpret the data efficiency and/or accurately. For example,
As used herein, the phrase “big data” may refer to data sets so large and/or complex that traditional data processing applications may be inadequate (e.g., to perform appropriate analysis, capture, data curation, search, sharing, storage, transfer, visualization, and/or information privacy for the data). Analysis of big data may lead to new correlations, to spot business trends, prevent diseases, etc. Scientists, business executives, practitioners of media and advertising and governments alike regularly face difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in meteorology, genomics, complex physics simulations, biological and environmental research, etc.
Note that data sets may grow in size because they are increasingly gathered by cheap and/or numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, Radio-Frequency Identification (“RFID”) readers, wireless sensor networks, etc.
Relational database management systems and desktop statistics and visualization packages may have difficulty handling big data. The work may instead be performed via parallel software running on multiple servers. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. The visualization server 150 may provide information, such as user customized reports and/or displays based on information in the big data database 110.
The visualization server 150 and/or other devices within the system 100 might be, for example, associated with a Personal Computer (“PC”), laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. The visualization server 150 may, according to some embodiments, be associated with an industrial asset enterprise.
According to some embodiments, an “automated” visualization server 150 may facilitate the collection and analysis of big data. For example, the visualization server 150 may automatically customize a display for a client platform 160. As used herein, the term “automated” may refer to, for example, actions that can be performed with little (or no) intervention by a human.
As used herein, devices, including those associated with the visualization server 150 and any other device described herein may exchange information via any communication network which may be one or more of a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.
The visualization server 150 may store information into and/or retrieve information from the big data database 110. The big data database 110 might be locally stored or reside remote from the visualization server 150. As will be described further below, the big data database 110 may be used by the visualization server 150 to facilitate a display of information to a user of one of the client platforms 160. According to some embodiments, the visualization server 150 communicates information associated with big data to a remote device and/or to an automated system, such as by transmitting an electronic file to a user device, an email server, a workflow management system, a predictive model, a map application, etc.
Although a single visualization server 150 is shown in
Note that the system 100 of
At S210, a big data pull infrastructure may provide a substantial number of electronic files, originating from a plurality of data sources, to be ingested and validated. The files may contain, for example, financial information about an enterprise or any other type of big data. As used herein, the term “enterprise” might refer to, for example, a business or any other type of organization. At S220, a visualization system may collect meta information associated with the electronic files received from the big data pull infrastructure. At S230, a hierarchical, multidimensional view of the meta data associated with the electronic files may be established.
At S240, the hierarchical, multidimensional view of the meta data is rendered by the visualization system as nested icons, at least one icon being represented via a plurality of unique visual characteristics indicating: (i) data that has not been ingested, (ii) data that has been ingested but not yet validated, and (iii) data that has been ingested and validated. According to some embodiments, this rendering is dynamically performed in substantially real time, the big data pull infrastructure is associated with a parent enterprise, and the hierarchical view of the meta data includes a set of child units operating under the parent enterprise. Each child unit might represent, for example, an operating business of the parent enterprise.
According to some embodiments, the multidimensional view of the meta data includes a plurality of operating parameters for each child unit. For example,
By way of example only, the operating parameters might be associated with: spend values, Cash Flow from Operating Activities (“CFOA”) values, and/or financial deflation values. Note that each operating parameter for the parent enterprise is visualized as a circular icon within a larger icon representing the parent enterprise. The display 300 also includes a child unit area 330 showing operating parameters for a number of different child units 340 (i.e., units A through D) and each operating parameter for a child unit 340 is visualized as a circular icon within a larger circular icon representing that child unit. According to some embodiments, sizes of operating parameter circular icons are associated with magnitudes of values of associated operating parameters. For example, a circle representing 8,000,000 (“8M”) may be larger as compared to a circle representing 4,000,000 (“4M”). In the example of
According to some embodiments, at least one icon is represented via a plurality of unique “visual characteristics” indicating: (i) data that has not been ingested, (ii) data that has been ingested but not yet validated, and (iii) data that has been ingested and validated. According to some embodiments, a “visual characteristic” may be associated with a perimeter line type, a perimeter line color, a perimeter line thickness, and/or a perimeter line animation (e.g., a portion of the perimeter line that flashes on and off). For example,
Thus, embodiments may provide methods, systems, and user interfaces to support a highly interactive application for the exploration of sourcing data available in a data lake. As used herein, the phrase “data lake” may refer to a massive, easily-accessible data repository that stores “big data” from several business entities within a large organization (or any other type of hierarchical data). Embodiments may provide a system to collect meta information about sourcing data while supporting an interactive user interface for exploring this meta-information. The collected meta information may be multidimensional and hierarchical in nature based on different businesses and sub-business (or any other type of organized data structures) and let users quickly slice and dice the multidimensional hierarchical data using a circular visualization.
Note that different building blocks of a proposed system, along with an existing data-lake infrastructure, may be used to create a visualization system. For example,
When the user interface 896 application is loaded, the user may see both an enterprise-wide overview and summaries by individual child units (e.g., businesses) represented as bubbles or circular icons. The size of labels may also be relative to the value of each child unit or business. According to some embodiments, clicking on a business bubble icon may take a user to another next level (e.g., which visualizes suppliers of each business). This next level may follow in the same fashion as the first level of the display. If a user wants to go back to the upper level, he or she might simply click the bubble icon of the business (e.g., Unit B) to return to enterprise-level display.
Note that
In addition to a data flow view, some embodiments may provide a data exploration view to graphically indicate a plurality of category icons, each icon representing a different type of data category, wherein nested sub-category icons are displayed within each category icon. For example,
According to some embodiments, movement of a computer pointer over a circular icon of sub-category may result in a real time update of the data flow view such that only flows of information from data sources to data destinations associated with that sub-category are rendered. For example,
According to some embodiments, a time line may be rendered graphically to indicate a period of time. For example,
According to some embodiments, the time line 1330 includes one or more graphical items associated with Events (“E”) that may occur in the system. For example, two events 1336 are illustrated in the time line 1330 of
Thus, embodiments may provide tools, systems and processes to support a highly interactive application for the exploration of available data inventory in a data-lake. The invention may provide an innovative interactive UI and effective technological solutions for exploring available data inventory of data across multiple businesses (or other operating units). Such an approach may improve the effectiveness of a user's ability to quickly access available data in a data lake and increase the ease with which he or she can identify what is available (and when to expect additional data pulls into the data-lake). Note that embodiments may be particular helpful when data is pulled into the data-lake from different data stream (i.e., existing data sources and/or real-time data streams). Moreover, embodiments may let a user easily consume meta-information about the data inventory and let him or her quickly identify the status of available data in a data lake.
The embodiments described herein may be implemented using any number of different hardware configurations. For example,
The processor 1510 also communicates with a storage device 1530. The storage device 1530 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 1530 stores a program 1512 and/or a visualization engine 1514 for controlling the processor 1510. The processor 1510 performs instructions of the programs 1512, 1514, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 1510 might arrange for a big data pull infrastructure to provide a substantial number of electronic files, originating from a plurality of data sources, to be ingested and validated. The processor 1510 may collect meta information associated with the electronic files received from the big data pull infrastructure. According to some embodiments, a hierarchical, multidimensional view of the meta data associated with the electronic files may be established by the processor 1510. Moreover, the hierarchical, multidimensional view of the meta data may be rendered by the processor 1510 as nested icons, at least one icon being represented via a plurality of unique visual characteristics indicating: (i) data that has not been ingested, (ii) data that has been ingested but not yet validated, and (iii) data that has been ingested and validated.
The programs 1512, 1514 may be stored in a compressed, uncompiled and/or encrypted format. The programs 1512, 1514 may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor 1510 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the apparatus 1500 from another device; or (ii) a software application or module within the apparatus 1500 from another software application, module, or any other source.
As shown in
The unit 1602 might be a unique alphanumeric code identifying 1602 a child unit operating under a parent enterprise, and the parameter 1604 might describe a type of value being tracked for the unit 1602. The status 1606 might indicate the current status of the data for the parameter 1604 (on a per-unit 1602 basis) and the meta information 1608 may adjust, for example, how a portion of the perimeter of a circular icon might be displayed to reflect that status 1606. In the example, of
Thus, some embodiments described herein may facilitate a visualization of big data in such a way so as to improve a person's ability to interpret the big data efficiently and/or accurately.
The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.
Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the databases and apparatus described herein may be split, combined, and/or handled by external systems).
Applicants have discovered that embodiments described herein may be particularly useful in connection with financial management systems, although embodiments may be used in connection other any other type of information (industrial assets, artificial intelligence, etc.).
While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.