Object-Centric Data Analysis Interface for Reusable Filtering
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Solution Overview
Problem
Existing data analysis systems lack an object-oriented methodology that efficiently tracks and displays the steps of down-selecting and filtering data objects, and re-applies such analysis to another selected set of objects, particularly in large data sets with many associated objects and complicated associations.
Innovation Solution
A system and user interface that provides an object-centric methodology for analyzing data sets, allowing users to select and drill down into groups of data objects, perform linked object searches, and track operations through a graphical interface, enabling efficient analysis and re-application of operations to new data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a time-series centric methodology is used for data analysis, then data manipulation and plotting are straightforward, but object associations and relationships cannot be efficiently analyzed before data analysis
Solution Approach 1:
The system segments the data analysis process into distinct phases: first analyzing object associations and relationships through the ontology, then performing time-series data manipulation and plotting. This segmentation allows each phase to be optimized independently, resolving the contradiction between ease of data manipulation and ability to detect object associations.
2Reliability
If all data objects are analyzed without prior selection, then comprehensive analysis is achieved, but analysis efficiency decreases due to large data sets with many associated objects
Solution Approach 1:
The system performs preliminary action by analyzing object associations and relationships before conducting the main data analysis. Users can pre-select and filter objects based on their relationships and associations, thereby improving analysis efficiency while maintaining comprehensive coverage of relevant data objects.
3Device complexity
If no system tracks and displays steps for down-selecting and filtering objects, then system simplicity is maintained, but user ability to navigate and re-apply analysis is compromised
Solution Approach 1:
The system implements feedback mechanisms that track and display the steps performed for down-selecting and filtering objects. This allows users to review previous operations, understand the analysis path, and re-apply analysis to different selected sets of objects, thereby improving ease of operation without excessive complexity.
4Productivity
If data analysis starts with selecting time-series data, then data manipulation is straightforward, but object-centric analysis and re-application to other objects is not enabled
Solution Approach 1:
The system provides universal tools and methodologies that can be applied across different object types and data sets. After performing initial data manipulation on selected time-series data, the same analysis tools can be re-applied to other objects in the ontology, enabling both efficient data manipulation and versatile object-centric analysis.
Data Source
AI summary
Methods and systems for generating and analyzing visualizations based on a group of sets of data objects. One system includes processors executing instructions to present the sets of data objects in a selectable format on a display device, receive a user selection of a first set of data objects, generate a user interface comprising an indication of the first set of data objects and a plurality of selectable tools to generate a first data visualization of the first set of objects from one or more operations to the first set of objects, receive a user selection of a second set of data objects, receive a user selection to cause the application of the one or more operations to the second set of data objects, and update the user interface to comprise a second visualization based on the one or more operations performed on the second set of data objects.