Interactive Data Visualization System for Big Data Analysis
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Solution Overview
Problem
Conventional data processing applications are not intuitive for less experienced users, failing to provide an easy way to analyze and visually present complex data sets, such as 'big data', which limits their ability to identify trends and relationships.
Innovation Solution
A data processing application that generates intuitive visualizations based on search results, allowing users to interact with these visualizations by applying filters, sorting, and aggregating data, using JavaScript Object Notation configurations, and enabling simultaneous updates across multiple visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional data processing applications are used to analyze large datasets, then data analysis capability is provided, but ease of operation deteriorates for less experienced users
Solution Approach 1:
The patent introduces visualizations as an intermediary layer between the complex data processing system and the user. These visualizations translate complex data relationships into intuitive graphical representations, allowing less experienced users to interact with and understand large datasets without needing to master complex analytical tools or methodologies.
Solution Approach 2:
The system automatically generates multiple types of visualizations ( histograms, event lines, line charts, link maps, heat maps, timelines, tables, and event block representations) based on search results, enabling users to obtain meaningful insights without manually configuring complex analysis parameters or selecting appropriate visualization types.
2Loss of information
If multiple visualizations are generated to represent different graphical representations of search results, then information completeness is improved, but device complexity increases
Solution Approach 1:
The patent segments the comprehensive data analysis into multiple specialized visualization types, each designed to highlight different aspects of the search results. By dividing the information presentation into distinct visual categories (spatial relationships, temporal patterns, frequency distributions, etc.), the system maintains information completeness while making each individual visualization simpler to interpret.
Solution Approach 2:
The system transforms complex multi-dimensional data relationships into various visual dimensions through different chart types. Each visualization type represents data from a different dimensional perspective, allowing users to comprehend complex relationships by viewing them across multiple visual dimensions rather than attempting to understand all relationships in a single complex view.
3Measurement precision
If filters and operations are applied to drill down into datasets, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs preliminary data processing and organization before presentation, automatically structuring large datasets into visualization-ready formats with pre-applied filtering and grouping. This preliminary action reduces the complexity of subsequent user interactions, allowing users to refine their analysis with simple filter applications rather than complex data manipulation operations.
Data Source
AI summary
Systems and methods are provided for analyzing data in one or more datasets. One or more data objects can be searched for within the one or more datasets. One or more visualizations can be generated based on the results of the search for the one or more data objects in the one or more datasets. When a user interacts with a visualization, e.g., by applying a filter, removing a filter, focusing on a particular subset of the one or more data object, etc., the visualization is updated automatically. Moreover, other visualizations generated based on the same search results may be simultaneously and automatically updated and presented to the user. Rather than a user having to analyze and consume data in a tabular format, the user can interact with representative visualizations to more readily discover and/or reveal aspects of the one or more data objects that would normally be hidden in the tabular format.