AU2020102300A4 - IAIP- Interactive Business Data: INTERACTIVE INTELLIGENT BUSINESS DATA VISUALIZATION USING AI- BASED PROGRAMMING - Google Patents

Abstract

Our Invention "IAIP- Interactive Business Data" is a method of creating a graphical representation of data in the form of a cloud data, Google Data, KDD-10, KDD-2020, heatmap the method including the all the steps of location data point, positioning data points , simulation data point, mapping Data point on a heatmap for graphical representation, calculating conical data distribution values around a data point based on a data value associated with that data point and rendering the heatmap based on the calculated data distribution values. The invented technology also an interactive system for visualizing business data structure, organized as per needed data mapping to the dimensional model allows to combine, merge, integrate data from more than one data source and also the data in the form of a graphical, location based data, mmulti-metric data visualization. The invented technology the visualizations are hierarchically structured and built from visualization elements including in ascending order of hierarchy, charts, panels, scenes and sheets and also the system provides a large variety of two- and three-dimensional visualization elements mostly predefined charts, graph, mAP which can be combined in any number into fully customized visualizations. The visualization is built by the system which prompts the user to select data sources data items to be visualized and certain features of the presentation layout and the response to the user input the system generates a number of candidate visualizations and presents them to the user for selection in descending order of preference based on numerical scores assigned to the candidate visualizations by a scoring algorithm. The data to be visualized can be filtered, drilled down for details, or presented in a series of consecutive visualizations, to create an animation effect. 20 49 Oct a Top0simeor206 2018 2y019 aar01 augusta ag Sep -5 3n9y sage 71, n, 1 3riny 19 e s AMmy Maye Seahs aa 3o201 R le outyAnm-21 M6sa Sg Tges WSW nTo 5 U&*a f 01 obyRa frM FIG 1 SHWSAN XAPLEOARHAMPUIGAPRO NW EHD

Description

Oct a

Top0simeor206 2018 2y019 aar01 augusta ag Sep -5 3n9y sage 71, n, 1 3riny s 19e

AMmy Maye

Seahs aa 3o201 R le outyAnm-21

M6sa Sg Tges WSW

nTo 5 U&*a f 01 obyRa frM

FIG 1 SHWSAN XAPLEOARHAMPUIGAPRO NW EHD

IAIP- Interactive Business Data: INTERACTIVE INTELLIGENT BUSINESS DATA VISUALIZATION USING Al- BASED PROGRAMMING

FIELD OF THE INVENTION

Our invention "IAIP- Interactive Business Data "is related to INTERACTIVE INTELLIGENT BUSINESS DATA VISUALIZATION USING Al- BASED PROGRAMMING and also to improved data visualization methods. The invention relates to a business data visualization system and method of visualizing business data organized according to the dimensional model.

BACKGROUND OF THE INVENTION

A chart or graph is described in Wikipedia as a type of information graphic or graphic organizer that represents tabular numeric data and/or functions. Charts are often used in an attempt to make it easier to understand large quantities of data and the relationship between different parts of the data. Charts can usually be read more quickly than the raw data that they come from. They are used in a wide variety of fields, and can be created by hand (often on graph paper) or by general purpose computers or specific computers using various different charting applications.

Traditional charts use well established and often poorly implemented ways of representing data. Many tools exist to help the user construct very sophisticated representations of data but that sophistication typically results in less meaningful charts. Embodiments of the present invention aim to overcome this problem.

It is known to use charting wizards such as those that are available in Excel and various other systems such as those provided by, for example, IBM. In addition, there are multiple Business Intelligence (BI) tools available to users to enable users to analyze data in an attempt to create meaningful feedback. However, as the amount of data increases, so does the complexity of the visual representations created by the analysis of the data. These complex representations can end up swamping parts of the visual representation that are most required and relevant to an end user.

One known method of visualizing data is the heatmap. A heatmap identifies the values of individual data points by allocating a specific color based on the data point value. For example, red may indicate that the data point value is high, and blue may indicate that the data point value is low. The color spectrum in between red and blue may then be used to indicate the interim values for relevant data points. The heatmap graphic is particularly useful for showing the position and intensity of certain data values with respect to other data values and within a defined environment, such as a geographical area, temporal period or other environment.

The field of business applications of computer technology has seen many important changes over the last few years. With steadily growing computational power and data storage capacities of computer systems used for business data processing, the interest of the business community has shifted from transactional data management systems (on line transaction processing systems, or OLTP systems, mostly supporting day-to-day business operations) and from relatively simple business data processing systems, towards sophisticated business management systems, such as enterprise resource planning (ERP) systems, integrating at the enterprise level all facets and functions of the business, including planning, manufacturing, sales and marketing. An example of a business management software package of this scope is SAP R/3 System available from SAP AG (Germany) or its U.S. branch, SAP America, Inc.

Among various alternative approaches to business data management and analysis developed over the last few years, many are related to data warehousing. A data warehouse can be defined broadly as a subject-oriented collection of business data identified with a particular period of time, as opposed to transactional databases dedicated to ongoing business activities. A scaled-down, usually single-subject oriented warehouse is sometimes referred to as a data mart. Data in a warehouse is normally gathered from a variety of sources (mostly various OLTP and legacy systems) and merged into a coherent whole. Data in a warehouse is usually stable, in that data is added to the warehouse but not removed. The latter feature, which is normally desirable to provide a more complete image of the business over time, may be absent from warehouses designed to keep data for a predetermined time span, with the oldest data being unloaded when the newest data is added.

As opposed to data stored in OLTP systems intended to support day-to-day operations and optimized for the speed and reliability of transaction updating, data stored in a data warehouse is intended to provide higher-level, aggregated views of the data, such as total sales by product line or region over a predetermined period of time, in support of business decision making. To provide consistently fast responses to such aggregate queries, data in a data warehouse or data mart must be structured in a manner facilitating the data synthesis, analysis, and consolidation.

The most characteristic feature of warehoused business data is its multidimensional view of a business, meaning that business data is organized according to major aspects and measures of the business, called dimensions, such as its products, markets, profits, or time periods involved, as opposed to data dependencies model of the business data, which keeps track of all logical relationships among all the possible data elements relevant to the business and its day-to-day operations. A dimension may include several hierarchical levels of categories, for example the market dimension may contain, in descending order, such categories as country, region, state, and city, each category having its own number of specific instances.

A hierarchical dimension reduces the total number of dimensions necessary to describe and organize the data, as compared with the situation where each category is represented by a separate dimension. The action of viewing data in greater detail by moving down the hierarchy of categories, i.e, by moving from parent to child category, is sometimes referred to as "drilling down" through the data. Quite naturally, the action of moving in the opposite direction, i.e., up the hierarchy of categories, to produce a more consolidated, higher-level view of data, is known as "drilling up" through the data.

Data organized according to the dimensional model are frequently visualized as a multidimensional data cube (or simply cube), a matrix-type structure having dimensions and their corresponding categories extending along its edges.

The volume of the cube is divided into cells, each cell corresponding to a combination of a specific instance of each dimension and containing a metric, such as a numerical value, corresponding to this combination. Such a structure has an obvious geometric representation and can be easily visualized only when the number of dimensions does not exceed three (and becomes a hypercube above this limit), but the term "cube" (or "multidimensional cube") is traditionally used for any number of dimensions. A source of business data organized according to the dimensional model is sometimes referred to as an OLAP source, from On-Line Analytical Processing, a term applied broadly to class of technologies designed for dimensionally-oriented, ad hoc data representation, access, and analysis.

PRIOR ART SEARCH

W02011081535A1*2009-12-142011-07-07Business Intelligence Solutions Safe Bevan improved method and system for calculating breakpoints in a data visualization system. US20130073480A1*2011-03-222013-03-2lLionel Alberta Real time cross correlation of intensity and sentiment from social media messages. US9472015B2*2012-05-152016-10-18Sap Se Real-time visualization of transactional data objects. US20150310647A1*2014-04-242015-10-29Sas Institute Inc. Techniques for Visualization of Data. W02002007007A1 *2000-07-172002-01-24Compudigm International Limited Contact centre data visualisation system and method. W02002008954A1*2000-07-102002-01-3lCompudigm International Limited Customer activity tracking system and method. W02002010979A1 *2000-07-312002-02-07Compudigm International Limited Warranty data visualisation system and method. W02002025494A1*2000-09-222002-03-28Compudigm International Limited Database query system and method. W02002042939A1*2000-11-242002-05-3oCompudigm International Limited Queue management system and method.

OBJECTIVES OF THE INVENTION

1. The objective of the invention is to a method of creating a graphical representation of data in the form of a cloud data, Google Data, KDD-10, KDD-2020, heatmap the method including the all the steps of location data point, positioning data points ,

simulation data point, mapping Data point on a heatmap for graphical representation, calculating conical data distribution values around a data point based on a data value associated with that data point and rendering the heatmap based on the calculated data distribution values.

2. The other objective of the invention is to the invented technology also an interactive system for visualizing business data structure, organized as per needed data mapping to the dimensional model allows to combine, merge, integrate data from more than one data source and also the data in the form of a graphical, location based data, mmulti-metric data visualization. 3. The other objective of the invention is to the invented technology the visualizations are hierarchically structured and built from visualization elements including in ascending order of hierarchy, charts, panels, scenes and sheets and also the system provides a large variety of two- and three-dimensional visualization elements mostly predefined charts, graph, mAP which can be combined in any number into fully customized visualizations. 4. The other objective of the invention is to the visualization is built by the system which prompts the user to select data sources data items to be visualized and certain features of the presentation layout and the response to the user input the system generates a number of candidate visualizations and presents them to the user for selection in descending order of preference based on numerical scores assigned to the candidate visualizations by a scoring algorithm. 5. The other objective of the invention is to the data to be visualized can be filtered, drilled down for details, or presented in a series of consecutive visualizations, to create an animation effect.

SUMMARY OF THE INVENTION

The invention provides a method of creating a graphical representation of data in the form of a heatmap, the method including the steps of positioning data points on a heatmap for graphical representation, calculating conical data distribution values around a data point based on a data value associated with that data point and rendering the heatmap based on the calculated data distribution values.

The invention provides a graphical computing system for generating a heatmap including: a data point calculation module arranged to position data points on a heatmap for graphical representation, and calculate conical data distribution values around a data point based on a data value associated with that data point, and a rendering module arranged to render the heatmap based on the calculated data distribution values.

The present invention provides an interactive system for visualizing business data organized according to the dimensional model, which system comprises means for retrieving data from at least one dimensional data source, means for converting the retrieved data into a graphical form, and means for presenting the converted data to a user as a graphical visualization. In a preferred embodiment the system of the present invention is computer-implemented and allows the user to combine data from more than one data source and present the data in the form of a graphical, multi-metric data visualizations.

Visualizations according to the invention are hierarchically structured and built from visualization elements including, in ascending order of hierarchy, charts, panels, scenes, and sheets. Any number of elements of a lower hierarchy level can be included in the visualization elements of the immediate higher hierarchy level.

A visualization according to the invention can be built in a number of manners, such as starting from a blank visualization and adding manually various visualization elements, in any order, or importing them from an existing visualization. According to a preferred embodiment, the visualization is built by the system, which prompts the user to select data sources, data items to be visualized, and certain features of the presentation layout. In response to the user input, the system generates a number of candidate visualizations and presents them to the user for selection in descending order of preference, based on numerical scores assigned to the candidate visualizations by a scoring algorithm.

The system of the invention provides the user with a large variety of predefined types of two- and three-dimensional charts, such as line, bar, surface, pie, swatch, and map charts, as well as two- and three-dimensional scenes, which can be combined, in any number, into fully customized visualizations. According to preferred embodiments of the invention, data are visualized with map charts and scorecards. Data to be entered into a visualization may be filtered, to temporarily limit the visualization to specific categories or ranges, or to animate the data by showing its progression over time, or across any other parameter. This is achieved by adding appropriate filters to the visualization, which filters can be turned on and off, as required.

BRIEF DESCRIPTION OF THE DIAGRAM

FIG. 1: shows an example of a heatmap using a prior known method.

FIG. 2A: is a system block diagram of a data visualization system.

FIG. 2B: shows a flow diagram for performing a heatmap generation method.

FIG. 3: shows a heatmap generated.

FIG. 4A: is a cross sectional view of a conic heatmap.

FIG. 4B: shows a plan view of the cross section shown in FIG. 4A.

FIG. 4C: is a cross sectional view of a conic heatmap with a drop off smoothing function.

FIG. 5: is a diagram showing schematically the hierarchical structure of the visualization.

FIG. 6-A:is a screenshot showing the first sheet of an exemplary two-sheet business data visualization according to the invention.

FIG. 6B: is a screenshot showing the second sheet of the visualization of FIG. 6A.

FIG. 7: is a screenshot illustrating an exemplary business data visualization using scorecards and a map chart.

DESCRIPTION OF THE INVENTION

The system includes at least a processor, one or more memory devices or an interface for connection to one or more memory devices, input and output interfaces for connection to external devices in order to enable the system to receive and operate upon instructions from one or more users or external systems, a data bus for internal and external communications between the various components, and a suitable power supply. Further, the system may include one or more communication devices (wired or wireless) for communicating with external and internal devices, and one or more input/output devices, such as a display, pointing device, keyboard or printing device.

The processor is arranged to perform the steps of a program stored as program instructions within the memory device. The program instructions enable the various methods of performing the invention as described herein to be performed. The program instructions may be developed or implemented using any suitable software programming language and toolkit, such as, for example, a C-based language. Further, the program instructions may be stored in any suitable manner such that they can be transferred to the memory device or read by the processor, such as, for example, being stored on a computer readable medium. The computer readable medium may be any suitable medium, such as, for example, solid state memory, magnetic tape, a compact disc (CD ROM or CD-R/W), memory card, flash memory, optical disc, magnetic disc or any other suitable computer readable medium.

The system is arranged to be in communication with external data storage systems or devices in order to retrieve the relevant data. It will be understood that the system herein described includes one or more elements that are arranged to perform the various functions and methods as described herein. The following portion of the description is aimed at providing the reader with an example of a conceptual view of how various modules and/or engines that make up the elements of the system may be interconnected to enable the functions to be implemented. Further, the following portion of the description explains in system related detail how the steps of the herein described method may be performed. The conceptual diagrams are provided to indicate to the reader how the various data elements are processed at different stages by the various different modules and/or engines.

It will be understood that the arrangement and construction of the modules or engines may be adapted accordingly depending on system and user requirements so that various functions may be performed by different modules or engines to those described herein. It will be understood that the modules and/or engines described may be implemented and provided with instructions using any suitable form of technology. For example, the modules or engines may be implemented or created using any suitable software code written in any suitable language, where the code is then compiled to produce an executable program that may be run on any suitable computing system. Alternatively, or in conjunction with the executable program, the modules or engines may be implemented using any suitable mixture of hardware, firmware and software. For example, portions of the modules may be implemented using an application specific integrated circuit (ASIC), a system-on-a-chip (SoC), field programmable gate arrays (FPGA) or any other suitable adaptable or programmable processing device.

The methods described herein may be implemented using a general purpose computing system specifically programmed to perform the described steps. Alternatively, the methods described herein may be implemented using a specific computer system such as a data visualization computer, a database query computer, a graphical analysis computer, a gaming data analysis computer, a retail environment analysis computer, a manufacturing data analysis computer, a business intelligence computer etc., where the computer has been specifically adapted to perform the described steps on specific data captured from an environment associated with a particular field.

As a particular example, the methods described herein may be applied or implemented using a gaming data analysis computer wherein a gaming environment is monitored and data associated with the gaming environment is stored in a data storage module and represented using a heatmap as described herein. The gaming data used to develop the heatmap may be related, for example, to the operations of a casino and may include, for example, data associated with the performance of various gaming machines and devices, as well as data associated with ATMs, cashiers, gaming tables, slot machines, electronic gaming machines, kiosks, related hotel devices etc.

As a further particular example, the methods described herein may be applied or implemented using a retail environment analysis computer wherein a retail environment is monitored and data associated with the retail environment is stored in a data storage module and is represented using a heatmap as described herein. The retail data used to develop the heatmap may be related, for example, to purchasing information associated with consumers, products sold in the retail environment, profit margins, purchasing costs, manufacturing costs, sale prices, consumer prices, the location of the retail environment and related premises, distribution data etc.

FIG. 1: is produced by rendering circles around a specific data point, where the color and diameter of the circle is based on a variable associated with the data point. Smaller diameter circles are used to represent smaller data point values and larger diameter circles are used to represent larger data point values. Further, the larger the circle (or data point value) the "hotter" the color used to represent the circle. For example, largest values may be rendered using large circles in the color red, whereas smallest values may be rendered using small circles colored blue. At points where the circles overlap, the circle associated with the largest data point value has priority over the smaller circles and the color for the larger circle is shown in the overlap region.

A first problem associated with this method of rendering the overlapped region is that only the values of data points representing the largest values are represented in the overlap regions and an accurate representation of the overlap regions is not provided. For example, two data points that are close to each other which represent a maximum value will be rendered using the same color as three or four (or more) data points that are close to each other which represent the same maximum value. A further problem occurs when data points representing smaller values are positioned next to or close to data points representing larger values as the rendered larger value data points obscure the rendered smaller value data points. This can therefore result in a loss of information being conveyed to the user as minimal data values may be considered as a very important part of the data analysis of the data set.

FIG. 2A: shows a system block diagram of a data visualization system specifically adapted to perform the various methods as herein described. A data storage module 201 provides data to a data retrieval module 203 upon request from the data retrieval module. That is, the data retrieval module 203 is configured to enable the retrieval of data from a data storage module 201, which is in communication with the data visualization system. The data storage module 201 may be any suitable type of data storage system. For example, it may be an enterprise data warehouse (EDW), a data mart, a database, a storage array or any other suitable device or groups of devices that can store data for later retrieval. Further, the data storage module 201 may be a cache memory used to temporarily store incoming data captured in real time.

A data point calculation module 205 receives or accesses the data from the data retrieval module 203 in order to calculate the values on and around each of the data points, as will be described in more detail below. In summary, the data point calculation module applies a geometric spatial distribution in the form of a substantially conic distribution about the data point based on the data point value. In this embodiment an exact conical distribution method is used. However, it will be understood that the terms cone, conic or conical distribution include other conic type distribution forms that may be applied other than the exact conic form in order to produce the advantages of the present invention. Indeed, the shape of the distribution around a data point may be tailored to the type of data being analyzed. Certain forms of data may be more suited to certain types of conic distribution in order to more accurately convey the data's properties.

Due to the use of the data point calculation module as described herein, an interpolation module is not required in order to calculate points in between known data points. Interpolation between data points is not required as the distribution calculations around a data point perform this task. This therefore provides a more efficient heatmap generation system.

A minima check module 207 performs a check on each of the calculated data point values for each of the data points to see whether the value generated for the data point using the data point calculation module 205 is greater than the actual value of that data point. This may occur where two data points are close to each other such that a first data point representing a large value swamps a second data point representing a smaller value. That is, if the heatmap were rendered, the minima value being represented by the second data point would not be shown after the data point calculation module has made the required calculations because the cone generated by the data point calculation module for the first data point engulfs the minima value of the second data point. The minima check module retrieves the original data for the data point and compares it with the calculated data point value.

If the minima check module 207 determines that the associated data point value has been hidden by an adjacent larger data point, a data point inverse calculation module 209 is activated to calculate an inverse data point value, as described in more detail below. That is, the data point inverse calculation module 209 receives the calculation data from the data point calculation module 205 and/or data retrieval module 203, and uses this data to calculate values on and around data points that represent minima values. These minima values are those that the minima check module detected have been hidden from view due to larger values associated with a nearby data point.

Optionally, a drop off smoothing function module 211 may be used to calculate drop off values for the edges of the calculated data points in order to provide a smoother looking image, as will be described in more detail below with reference to FIG. 4C. The output of the drop off smoothing function module is communicated to the rendering module 213. A rendering module 213 receives the output of the data point calculation module, inverse data point calculation module and drop off smoothing function module (if used) to render a suitable heatmap image using standard rendering techniques. This rendered heatmap image is then provided to an output module 215. That is, the rendering module uses the values calculated by the various modules to create a suitable output signal or file that enables a heatmap image to be rendered on the desired output module.

The output module may be a display device, such as a standalone display unit, or a display unit integrated with a laptop computing device, PC, hand held computing device, or other computing system or display system. As an alternative to, or in conjunction with, the display module, further output modules may be provided to output the rendered image. That is, the raw data retrieved by the data retrieval module may be analyzed and converted to provide output data in a specific format. The output data may be provided to the display and/or further output modules to enable a user to visualize the raw data in a manner that conveys clear and concise information that is easy to be interpreted and understood.

The further output module may form part of a printing device in communication with the described system to receive print control data so that representations of the data may be printed on any suitable print medium. Alternatively, the further output module may be an interface that enables the data output to/from the rendering module to be interfaced with other data handling modules or storage devices. As a further alternative, the output module may be the same or different data storage module as described above.

FIG. 2B: is a flow diagram for producing a heatmap according to this embodiment. At step S201, the data retrieval module retrieves data from the data storage module. At step S203, data point values are calculated for and around each data point using the data point calculation module distribution calculations. At step S205, a minima data check is performed by the minima check module to see whether any of the data point values calculated by the data point calculation module exceed the actual data value that the data point is supposed to represent.

At step S207, for each data point that is detected as having a minima data value that is hidden, an inverse data point value is calculated by the inverse data point calculation module using an inverse distribution. At step S209, the drop-off values are optionally calculated using the drop-off smoothing function module at the edge of data points to provide smoothing. At step S211, the heat map image is rendered by the rendering module using the values calculated by the data point calculation module and inverse data point calculation module, and optionally the drop off smoothing function module.

At step S213 the rendered image produced by the rendering module is output to the output device. There are certain key elements that a graphical visualization system designer should bear in mind when determining how and in what form a graphical image is to be visualized. For example, the graphical image should be visually pleasing to the user eye, understandable to the user such that it conveys relevant information about the data being represented and able to be efficiently produced by the system. The method of producing the image using a conic representation as described herein is far more efficient than those previously used to produce heat maps, such as that shown in FIG. 1 for example.

FIG. 3: shows a heatmap produced by a conic representation method as described herein. The image in FIG. 3 is very pleasing to the eye in nature. This is mainly due to its organic appearance even though the methods used to create the image are non-organic. Organic representations are generally known to be easier to read. Further, the image still conveys the information (which is based on the data points used) in a clear and succinct manner.

Further, the methods used to produce the image as herein described are far more efficient than the prior known methods used for producing heat maps, such as those shown in FIG. 1.

The cone shaped distribution method used in this embodiment for rendering the image is described in more detail below with reference to FIGS. 4A, 4B, 4C, 5A and 5B. FIG. 4A shows a cross sectional view representative of a heatmap representation according to this embodiment. Data point value distributions are applied to and around each data point. According to this particular embodiment, the data point values are distributed using an exact cone shaped distribution. Cone distribution 301 is calculated to represent data for data point 303, cone distribution 305 is calculated to represent data for data point 307 and cone distribution 309 is calculated to represent data for data point 311. Each cone distribution is sized according to the value of the data being represented at the associated data point.

According to this embodiment, the internal angle 311 between the base and side of each cone distribution is fixed. Therefore, as the data point value increases and decreases, the height and base of the cone varies. According to this embodiment, the angle is fixed at 45°. It is preferable that the angle used is between 40 and 50°. However, it will be understood that any suitable angle may be used where the data point values are distributed around the data point. It will be understood that, as an alternative, the internal angle may be variable such that different value data points are represented by higher cones using a larger internal angle and smaller value data points are represented by shorter cones using a smaller internal angle. It will be understood that the base of the cone distribution for this alternative is fixed for each cone.

It can be seen in FIG. 4A that cone distribution 301 has a higher data point value than both of cone distributions 305 and 309. Further, cone distribution 305 has a higher data point value than cone distribution 309. That is, cone 301 is representative of a data point with a higher value than the data points which cones 305 and 309 represent. Each of the conic distributions effectively provides each data point with a further dimension in which to represent a variable "Z". That is, the further dimension is the "height" of the data point, which indicates the "Z" variable for the data point being represented.

The values for each data point on the heatmap may be represented using colors or grey scales wherein the value calculated by the data point calculation module for each data point is represented using a range of colors or grey scales and the color chosen from the scale varies based on the calculated value for the distribution used. According to this embodiment, a color scale 313 is provided to graphically indicate the height of the cone for each data point. The scale is the spectrum, from Red, through to Blue, including Orange, Yellow and Green in sequence.

For example, the height of cone 301 is indicated in FIG. 4A as being level with the Red scale, and so the cone 301 will be rendered in Red. The height of cone 305 is indicated as being level with the Orange/Yellow scale, and so will be rendered using those colors accordingly. The height of cone 309 is indicated as being level with the Green scale and so will be rendered using a Green color. It will be understood that the appropriate colors used during the rendering stage may be determined from a look-up table. In order to take into account, the interaction of two overlapping cones, such as, for example, the cone distributions 301 and 305, color values are calculated by the data point calculation module and smoothed (or accumulated) for the overlapping region 315 on the heatmap. That is, the total value of the overlap is taken into account to indicate the cumulative (or additive) effect of the overlap.

Referring to FIG. 4A the cumulative sum of the heights (as indicated on the y-axis) of the cones is calculated using the data point calculation module across the x-axis to produce a cumulative value 317 for the combined cones. It will be understood that the height of the cones shown in FIG. 4A represent a specific variable value associated with the data points.

The cumulative effect of the overlap of two distribution regions is represented in the heatmap by a line 317, where the color of the line is determined by calculating the accumulated values of the crossed over distributions 301 and 305. The calculated accumulated value is then used to render the line in the associated color. In this example, the area on the heatmap representing this crossover portion is rendered using a color within the color range that aligns with accumulated value rather than the value that aligns with the exact point of cross over. As an alternative, the overlap between the two distribution regions may be represented by using the higher of the two distribution values. That is, a cumulative or additive effect is not produced, but instead the outline of the distribution is maintained to indicate the higher of the distribution values of either of the overlapping distributions.

Optionally, a drop off function may be applied by the drop off smoothing function module to the edges of the cones to allow the values to drop more smoothly from the cone region into the areas 319 of the map that have no distribution. The steepness of the drop off function may be based on the height of the cone to enable a steeper drop off for shorter cones, and a shallower drop off for taller cones. For example, referring to FIG. 4C, a cross sectional view of a conic heatmap with a drop off smoothing function can be seen. The cone distribution 305 includes drop off smoothing lines 321A and 321B calculated by the drop off smoothing function module. The cone distribution 309 includes drop off smoothing lines 321C and 321D also calculated by the drop off smoothing function module. Therefore, the colors used to represent the edges of the cones where they meet the minimal value (or background value) associated with the heatmap drop off more smoothly or in a shallower manner to provide a more aesthetic graphical image. The drop off smoothing function may be any suitable smoothing calculation. For example, a Gaussian blur function as described at en.wikipedia.org/wiki/Gaussian blur may be used.

FIG. 4B shows a plan view of the cross section shown in FIG. 4A. The base of cone 301 is displayed next to the base of cone 305, with an overlap portion 315. The color of the base of cone 301 is red, which then blends into the color of cone 305, which is green/yellow. There is a cumulative effect at the overlap section as described above. the visualization according to the invention, sheets, scenes and panels are normally used to group related information, which is presented in the form of charts. A new sheet, scene, or panel is typically added to the visualization when there is a thematic change, for example from sales to marketing, when the existing visualization elements contain too much information, or a reference is made to a new data source. There is no limit to the number of visualization elements which may be included in any given level of hierarchy, other than resources of the computer system used for creating and viewing the visualization.

In particular, a visualization may contain any number of sheets, a sheet any number of scenes, a scene any number of panels, and a panel any number of valid chart combinations. In practice, however, the number of visualization elements at any given level of hierarchy is limited by the visualization space offered by visualization elements of the immediate and higher levels and by other possible design considerations. For example, the space offered by a sheet may be limited by design to the area of the display device, such as the monitor screen, to avoid scrolling. This automatically affects the number of visualization elements entered at each of the lower levels of hierarchy, to avoid the screen space becoming overcrowded with scenes and panels, or graphs becoming too small and illegible. Increasing the number of sheets to increase the amount of the available visualization space may not always be a solution, as this may result in the visualization losing its focus.

The method and the system of the invention provides the user with choice of two- and three-dimensional panels and a large variety of two- and three dimensional charts, which can be combined into multi-metric, fully customized visualizations. These visualizations may include data from more than one data source and combine both two- and three dimensional panels in one scene, similarly as two- and three-dimensional charts in one panel.

Chart, the basic (lowest hierarchy level) visualization element, is at the same time the most important single element responsible for summarizing the data and conveying its meaning to the user. Charts are used, for example, to show comparisons, trends, and relationships between data items, Charts are placed on panels and all charts on the same panel must reference to the same data source and have a common axis. Any charts within the same sheet using the same textual data item on an axis may have these axes synchronized, meaning that any changes to one axis result in the remaining axes being concurrently updated.

The method and the system of the invention provide a large variety of predefined types of charts to be used for visualization purposes, including, but not limited to, scatter plot charts, line charts, bar (single, stacked, or clustered) charts, pie charts, surface charts, swatch charts, value charts, gauge charts, table charts, crosstab charts, matrix charts and map charts (maps). Most of these charts may be either two- or three dimensional. Some, such as surface charts, are always three-dimensional.

Charts can be combined, manipulated, and enhanced in a number of manners, to increase their informative value and visual appeal. For example, charts of either the same or different types can be combined in a single panel, to create multi-metric, comparison, or correlation charts. Such combinations include, for example, a multi-line chart (combination of several line charts), a multi-surface chart (combination of several surface charts), combination of several scatter plots (either two- or three-dimensional), a combination of a two-dimensional bar chart with a two-dimensional line chart, a combination of a three-dimensional bar chart with a three-dimensional surface chart, etc. Data values can be represented as numbers on a panel, to provide information in a non graphical format (a value chart). Such numerical values (value charts) can be combined with other chart types, for example the swatch chart, to reinforce their informative value. In some types of charts, numeric data ranges may be mapped to colors or multiple axes may be used to analyze several data items at a time (a parallel coordinate chart). These and some other options available to the user will be discussed later in more detail.

Color can be used in charts and in backgrounds of panels and scenes to enhance their informative value and visual appeal. In many chart types, such as multi-line and cluster bar charts, color can be used to represent grouped data items. In pie charts, colors are used to differentiate between various sectors of the chart. Colors may also be used to highlight ranges of data and exceptions. These colors, which can be changed at any time in the process of creating a visualization, belong to a color palette, understood as a set of colors on a scale. A color palette can be either continuous (gradient), with colors blending into each other along a chromatic scale, or discrete (non-gradient), with colors distinctly divided along a chromatic scale. The method and system of the invention provides several default color palettes of both kinds. These palettes may be modified by the user and new palettes may be created. Color palettes may also be imported from existing visualizations.

The choice of chart to visualize data depends mostly on the nature of data to be charted and the information that the chart is intended to communicate. This is why it is usually advisable to define first the purpose of the visualization, then to select the most effective chart to illustrate it. For example, when the purpose is to show the change over time for one variable, the bar or line chart is usually the best choice. Tracking data over time, comparing trends and cycles or showing a time series analysis is usually best visualized by using the line or multi-line chart.

The three-dimensional bar chart is usually used to compare the relationship between two data items, whereas comparing relationships between several data items may be best visualized by the surface or multi-line chart. Comparing parts of a whole as a percentage or groups of information over time is normally best visualized by the stacked bar chart, whereas comparing groups of related information requires the use of the clustered bar chart. The pie chart is usually used to represent the parts of a whole. Creating a scorecard of key performance indicators is best achieved using the parallel coordinate chart or swatch chart, the latter usually in combination with the value chart. The parallel coordinate chart is also used to compare or normalize data using multiple axis ranges. Correlation between various data items may be shown by the scatter plot chart, the multi line chart, the bar and line chart, or the multi-surface chart. Geographically aggregated data is best visualized using a map (map chart).

The panel, a visualization element one level above the chart in the visualization hierarchy, is a flat, usually rectangular and framed surface, used as an immediate holder of a chart or charts. A panel contains information from only one data source, but may contain multiple charts referencing that data source. Similarly, more than one panel in a visualization may refer to the same data source. Depending on its position in a scene, a panel may be either vertical or horizontal. After being inserted into a scene, panels may be moved (repositioned) and/or resized, and labels, background images, and/or colors may be added thereto, to increase their informative value and visual appeal.

The scene, a visualization element one level above the panel in the visualization hierarchy, has no well-defined limits and can be seen as two- or three-dimensional window (visualization space) that holds a panel or a group of panels. Usually a scene contains panels and charts that are thematically linked, such as charts for the revenue, profit margins, and costs for one department. Multiple scenes can be put on one sheet. The sheet is a page in a visualization file, similar in nature to sheets in a spreadsheet file, and usually combines several thematically related scenes. New sheets are added to the visualization to increase the amount of the visualization space and/or to separate various aspects of the common visualization theme.

The visualization system and method according to the invention is implemented in the form of an application running under an operating system, preferably under the MS Windows operating system, using facilities and methodologies of the Windows environment well known to those skilled in the art, such as the point-and-click graphical user interface, as well as standard input and output devices, such as a mouse and a keyboard. The computer running the application is normally linked to a computer network, to facilitate access to sources of the visualized data, which data may be stored on a remote computer (server) connected to the same network. The application is used to create new and to edit existing visualizations, which may be then distributed over the network for viewing by the end users (consumers), using either the same application or other suitable software. In a preferred embodiment, the visualizations are distributed by a server as HTML files over a TCP/IP network, preferably an intranet or the Internet, and are viewed using a client software and a Web browser.

FIG. 5. The visualization shown diagrammatically in FIG. 5 consists of k sheets (sheets 1 through k). Of those, the second sheet (sheet 2) consists of 1 scenes (scenes 1 through 1). The lth sheet (sheet 1) contains m panels (panels 1 through m), of which the mth panel (panel m) includes n charts (charts 1 through n). To avoid overcrowding, FIG. 1 shows visualization elements of an immediate lower level of hierarchy only for one visualization element of the immediate higher level of hierarchy. It is obvious, however, that this tree-like structure extends to each visualization element of each level of hierarchy above the level of chart. There are no limits and rules to follow when choosing the number of visualization elements of a given level of hierarchy when creating a visualization. In an extreme case, the visualization may even consist of a single chart (k=1=m=n=1 in the diagram of FIG. 5).

The invention, sheets, scenes and panels are normally used to group related information, which is presented in the form of charts. A new sheet, scene, or panel is typically added to the visualization when there is a thematic change, for example from sales to marketing, when the existing visualization elements contain too much information, or a reference is made to a new data source. There is no limit to the number of visualization elements which may be included in any given level of hierarchy, other than resources of the computer system used for creating and viewing the visualization. In particular, a visualization may contain any number of sheets, a sheet any number of scenes, a scene any number of panels, and a panel any number of valid chart combinations.

In practice, however, the number of visualization elements at any given level of hierarchy is limited by the visualization space offered by visualization elements of the immediate and higher levels and by other possible design considerations. For example, the space offered by a sheet may be limited by design to the area of the display device, such as the monitor screen, to avoid scrolling. This automatically affects the number of visualization elements entered at each of the lower levels of hierarchy, to avoid the screen space becoming overcrowded with scenes and panels, or graphs becoming too small and illegible. Increasing the number of sheets to increase the amount of the available visualization space may not always be a solution, as this may result in the visualization losing its focus.

FIGS. 6A and 6B provide an example of a two-sheet visualization according to a preferred embodiment of the invention. FIG. 6A shows the first sheet (1) of the visualization. Sheet I is identified by its title appearing on a tab 2 at the bottom of the screen. A tab 3 identifies the second (invisible in the background) sheet of the visualization. This sheet can be brought to the foreground by clicking at tab 3, which action hides sheet 1 in the background. Sheet 1 contains only one three-dimensional scene 4 composed of three vertical panels 5, 6, and 7 and a horizontal panel 8.

Each panel holds a single chart (9, 10, 11, and 12, respectively), which charts show various aggregated business performance characteristics, identified by labels 13, 14, 15, and 16 associated with panels 5, 6, 7, and 8, respectively. Of the charts shown in FIG. 6A, chart 9 is a two-dimensional clustered bar chart, chart 10 is a two-dimensional pie chart, chart 11 is a two-dimensional bar chart combined with a two-dimensional line chart, and chart 12 is a three-dimensional bar chart. An icon 17 in the lower left-hand comer of sheet 1 is a minimized legend window, explaining the color-coding scheme of the graphs. FIG. 6B: shows the second sheet (21) of the visualization, which sheet was hidden behind sheet 1 in FIG. 6A. Sheet 21 is now the foreground sheet, identified by tab 3. Tab 2 identifies sheet 1 (shown in FIG. 6A), now invisible (hidden behind sheet 13).

As previously, the background sheet 1 can be brought to the foreground by clicking at tab 2, which action hides sheet 21 behind sheet 1. Sheet 21 contains only one two dimensional scene 22 composed of three panels 23, 24, and 25, each holding a single chart 26, 27, and 28, respectively. Charts appearing in FIG. 6B are a two-dimensional bar chart combined with a line chart (chart 26), a multi-line chart 27 and a parallel coordinate chart 28. Window 29 is a legend window explaining the scheme of color-coding used for the charts. Window 30 is an Explain Window, showing either explanatory notes relating to the sheet, scene, or panel or data details for a chart. The notes for a given visualization element can be viewed, entered, or edited by selecting (clicking at) the visualization element of interest. Data details for charts can only be viewed.

The invention, data to be included in a visualization may be filtered, i.e., limited to a subset of data available from a given source. Filtering may be necessary, for example, to focus the visualization on aspects of the business which are of interest. For example, instead of looking at total sales, viewing sales for a specific region, product line, or time segment may be preferable. In some cases, trends and patterns may be more evident when limiting the amount of data included in the visualization. In particular, it may happen that most of the relevant data is within a particular range, so it may be desirable to highlight or narrow in on that range. Sometimes it may be desirable to animate some of the data in a sequence, to highlight a chronological or geographic pattern. The method and system according to the invention provides several types of predefined filters which serve the above stated purposes, including, but not limited to, check filter, radio filter, range filter, and animation filter.

The check filter provides a selection of categories (data items) from the data source. For example, for a chart showing profit margin versus products, it may be desirable to chart the results for some locations but not others. This is achieved by inserting a check filter associated with the chart, choosing location as the data item and selecting the required locations. Multiple data items may be selected at different levels of the data source, except for parent and child categories of the same dimension, and do not have to be contiguous.

FIG. 7: a matrix swatch chart 41 (shaded circles) is used to summarize key performance indicators (profit margin, revenue, and product cost, corresponding to rows of the matrix) for three different lines of products (outdoor products, environmental line of products, and GO sport line of products, corresponding to columns of the matrix). In the original display, the swatch chart is color-coded using three discrete color palettes of three colors each, mapping predetermined numerical ranges of performance indicators to specific colors. The colors of each palette, but not the corresponding numerical ranges, are the same and may be linked to qualitative, non-numerical characteristics, such as bad, good, and exceptional. This coding allows to make a quick distinction between good and bad performances of various products. A value chart is layered on top of the swatch chart, to show specific values for each product and each indicator, thus adding an additional level of numerical detail to qualitative information provided by the color coding.

A parallel coordinate chart 42 in FIG. 7 shows essentially the same information as the matrix swatch chart 41 but in a different manner. In the parallel coordinate chart 42, the first axis is a product line axis, whereas the remaining axes represent the key performance indicators. The scale of each axis is adjusted to fit the length of the axis, which length is the same for all axes (normalized). Each line plot in the chart represents one product line, starting at the leftmost axis at a point corresponding to the given product line and continuing through points on the following, parallel and equally spaced axes, which points correspond to the value of a given indicator for the product line. In the original display, product lines are color-coded, meaning that each line plot is of a different color corresponding to the color coding for that product line.

A map chart 43 in FIG. 7 show percentage of the planned number of units of a product sold in major US cities. The map show boundaries of the country and its states, with cities represented by circular spots. In the original display, these spots are color-coded using a continuous palette, with colors representing the percentage of plan for a given city. The color palettes for all the charts appearing in the sheet are shown in a legend window 44.

Claims (4)

WE CLAIM:

1) Our Invention "IAIP- Interactive Business Data" is a method of creating a graphical representation of data in the form of a cloud data, Google Data, KDD-10, KDD-2020, heatmap the method including the all the steps of location data point, positioning data points , simulation data point, mapping Data point on a heatmap for graphical representation, calculating conical data distribution values around a data point based on a data value associated with that data point and rendering the heatmap based on the calculated data distribution values. The invented technology also an interactive system for visualizing business data structure, organized as per needed data mapping to the dimensional model allows to combine, merge, integrate data from more than one data source and also the data in the form of a graphical, location based data, mmulti-metric data visualization. The invented technology the visualizations are hierarchically structured and built from visualization elements including in ascending order of hierarchy, charts, panels, scenes and sheets and also the system provides a large variety of two- and three dimensional visualization elements mostly predefined charts, graph, mAP which can be combined in any number into fully customized visualizations. The visualization is built by the system which prompts the user to select data sources data items to be visualized and certain features of the presentation layout and the response to the user input the system generates a number of candidate visualizations and presents them to the user for selection in descending order of preference based on numerical scores assigned to the candidate visualizations by a scoring algorithm. The data to be visualized can be filtered, drilled down for details, or presented in a series of consecutive visualizations, to create an animation effect.

2) According to claim# the invention is to a method of creating a graphical representation of data in the form of a cloud data, Google Data, KDD-10, KDD-2020, heatmap the method including the all the steps of location data point, positioning data points, simulation data point, mapping Data point on a heatmap for graphical representation, calculating conical data distribution values around a data point based on a data value associated with that data point and rendering the heatmap based on the calculated data distribution values.

3) According to claim,2# the invention is to the invented technology also an interactive system for visualizing business data structure, organized as per needed data mapping to the dimensional model allows to combine, merge, integrate data from more than one data source and also the data in the form of a graphical, location based data, mmulti-metric data visualization.

4) According to claiml,2,3# the invention is to the invented technology the visualizations are hierarchically structured and built from visualization elements including in ascending order of hierarchy, charts, panels, scenes and sheets and also the system provides a large variety of two- and three-dimensional visualization elements mostly predefined charts, graph, mAP which can be combined in any number into fully customized visualizations. ) According to claim1,2,4# the invention is to the visualization is built by the system which prompts the user to select data sources data items to be visualized and certain features of the presentation layout and the response to the user input the system generates a number of candidate visualizations and presents them to the user for selection in descending order of preference based on numerical scores assigned to the candidate visualizations by a scoring algorithm. The invention is to the data to be visualized can be filtered, drilled down for details, or presented in a series of consecutive visualizations, to create an animation effect.

FIG. 1: SHOWS AN EXAMPLE OF A HEATMAP USING A PRIOR KNOWN METHOD.

FIG. 2A: IS A SYSTEM BLOCK DIAGRAM OF A DATA VISUALIZATION SYSTEM.

FIG. 2B: SHOWS A FLOW DIAGRAM FOR PERFORMING A HEATMAP GENERATION METHOD.

FIG. 3: SHOWS A HEATMAP GENERATED.

FIG. 4A: IS A CROSS SECTIONAL VIEW OF A CONIC HEATMAP.

FIG. 4B: SHOWS A PLAN VIEW OF THE CROSS SECTION SHOWN IN FIG. 4A.

FIG. 4C: IS A CROSS SECTIONAL VIEW OF A CONIC HEATMAP WITH A DROP OFF SMOOTHING FUNCTION.

FIG. 5: IS A DIAGRAM SHOWING SCHEMATICALLY THE HIERARCHICAL STRUCTURE OF THE VISUALIZATION.

FIG. 6-A:IS A SCREENSHOT SHOWING THE FIRST SHEET OF AN EXEMPLARY TWO-SHEET BUSINESS DATA VISUALIZATION ACCORDING TO THE INVENTION.

FIG. 6B: IS A SCREENSHOT SHOWING THE SECOND SHEET OF THE VISUALIZATION OF FIG. 6A.

FIG. 7: IS A SCREENSHOT ILLUSTRATING AN EXEMPLARY BUSINESS DATA VISUALIZATION USING SCORECARDS AND A MAP CHART.