# US 10,769,162 B2 · Generating business intelligence analytics data visualizations with genomically defined genetic selection

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- Number: US 10,769,162 B2
- Document ID: US10769162B2
- Status: Granted
- Kind: B2
- Application number: 15/460,664
- Filed: 2017-03-16
- Published: 2017-06-29
- Granted: 2020-09-08
- Earliest claimed: 2015-11-03
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Daniel J. Rope; Graham J. Wills
- Web page: https://patentgrep.com/patent/US10769162B2

## CPC

- G06F 16/248: Presentation of query results
- G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)}
- G06F 16/904: Browsing; Visualisation therefor (for navigating the web G06F16/954; browsing optimisation for the web G06F16/957)
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Techniques are described for genomically defining digital genes encoding data visualization elements and potential incremental changes to the elements as the basis for a genetic selection process for automated generating of data visualizations. In one aspect, a method includes receiving set of input data. The method further includes generating digital genes that genomically define data visualization elements based on the input data, and that define potential incremental changes to the data visualization elements. The method further includes executing a genetic selection process with respect to one or more fitness functions on populations of candidate data visualizations that are based on the genomically defined data visualization elements. The method further includes outputting final data visualization output generated by the genetic selection process.

## Claims

### Claim 1

A method in a business intelligence system having a multi-tier architecture, each tier comprising one or more respective servers, the method comprising:
receiving, with one or more processing devices via an application tier of the multi-tier architecture comprising a data access service, a set of input data comprising one or more bodies of data from a database tier of the multi-tier architecture comprising database servers;
generating, with the one or more processing devices via the application tier of the multi-tier architecture further comprising enterprise applications, one or more digital chromosomes each comprising a collection of digital genes that genomically define data visualization elements based on the input data, wherein each digital gene of a respective digital chromosome defines an incremental change to at least one of the data visualization elements, and wherein each digital gene of the respective digital chromosome comprises a data field indicative of a first body of data from the one or more bodies of data of the input data to be added as an axis of a data visualization, the data field being associated with its defined incremental change;
based on the data field, mapping, with the one or more processing devices via the application tier of the multi-tier architecture comprising the enterprise applications, data from the first body of data included in the input data that is defined by the data field to one or more graphics properties in at least one of the data visualization elements genomically defined by the collection of digital genes;
executing, with the one or more processing devices via the application tier of the multi-tier architecture comprising the enterprise applications, a genetic selection process with respect to the one or more fitness functions on populations of candidate data visualizations that are based on the genomically defined data visualization elements;
generating, with the one or more processing devices via the application tier of the multi-tier architecture comprising the enterprise applications, and based on execution of the genetic selection process with respect to the one or more fitness functions, a final data visualization from the populations of candidate data visualizations; and
outputting, with the one or more processing devices via a web tier of the multi-tier architecture comprising web server, the final data visualization generated by the genetic selection process.

### Claim 2 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome comprises a descriptor that describes a purpose of an action that causes the respective incremental change to the respective at least one of the data visualization elements.

### Claim 3 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome comprise data fields enabled to map data from the input data to graphic properties in data visualization elements defined by the genes.

### Claim 4 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome comprise a respective option index indicating one of a set of options associated with a particular type of variation in the respective incremental change to the respective at least one of the data visualization elements.

### Claim 5 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome determines a type of data visualization element with which to represent a portion of data from the input data.

### Claim 6 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome determine a coordinate system with which to represent a portion of data from the input data.

### Claim 7 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome determine a shape with which to represent a portion of data from the input data.

### Claim 8 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective chromosome determine a color with which to represent a portion of data for the input data.

### Claim 9 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome determine a type of layout with which to represent at least a portion of data from the input data.

### Claim 10 (depends on 1)

The method of claim 1, wherein one or more of the digital genes of a respective digital chromosome determine a system for aggregating portions of data from the input data.

### Claim 11 (depends on 1)

The method of claim 1, wherein executing the genetic selection process comprises iteratively generating the populations of candidate data visualizations and down-selecting from among the populations with respect to the one or more fitness functions, and wherein generating the populations of candidate data visualizations is based on combinations of the digital genes in the one or more digital chromosomes initially as generated and subsequently based on new combinations of digital genes from the down-selected populations.

### Claim 12 (depends on 1)

The method of claim 1, further comprising generating digital chromosomes comprising organized sets of the digital genes sufficient to genomically define complete sets of the data visualization elements to compose the candidate data visualizations.