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Smart Analytic of Data Contribution in Visualizations Disclosure Number: IPCOM000245291D
Publication Date: 2016-Feb-26
Document File: 7 page(s) / 146K

Publishing Venue

The Prior Art Database


Disclosed is a breakthrough approach to smart data analysis for supported devices with available zooming, focus, point, or touch options. This includes a dynamic method to enhance analytics using a base relevant visualization that can be customized targeting specific area of the impacted elements to perform further root cause analysis.

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Smart Analytic of Data Contribution in Visualizations

As emerging reporting and query software are fast evolving toward mobile and cloud based approaches, data management and presentation are indispensable factors. End users are accustomed to new navigational formats in display devices. Users also expect a new approach to finding solutions that answer business questions and support the decision making process to the extent of a data point intersection of interest. Enterprises are dependent on some type of visualization of data, rather than raw data, for a quick reference and understanding the big picture and relationships among parts.

Graphical representation of data can become complex if it is not designed to answer specific questions . Currently, in the legacy software, the choices are to create multiple dashboards, manually drill to further insight, or use multiple embedded controls to filter and customize the view.

A method is needed to empower end users to identify immediate correlations and gain access into data point exploration simply by focusing into areas of interest through visualization.

The novel method enables user to point to and zoom in and out of a specific area of interest /data point in a dashboard or graph

when a dynamic root-cause analysis is performed, and then see it presented in a pop-up format that can be pinned for reference or further analysis. This prevents additional drilling, expansion of data, and losing the original visualization/content for a cross reference comparison.

With this dynamic method, the user uses user interface (UI) gestures to interact with data points on the visualization. Based on the data model that defines the relationship between the data items , the system determines the data items that are contributing to the data point selected by the user.

In an example embodiment, a company's profit has increased in a year for the product line. The company is interested to know the contributing factors, which will allow the company to accordingly adjust the inventory and sales projections for the next year. The initial indication is that the food category is the major contributor in Q4, against a measure in a general dashboard.

Following are the implementation steps:

1. The system queries the data, detects relationships in the impact diagram related to selected data point, and reaches a point at which it detects the combined contributing factors impacting the selected data point ; it detects the contributing factor through the impact diagram starting with the measure (Figure 1)


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Figure 1: The impact diagram, displaying mechanics of how this process can be done

2. The system displays the result in a pop-up visualization that shows the foundation and most valuable contributors impacting the change in the selected data point (Figure 2)

Figure 2: Pop-up visualization


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3. Pr...