Joined Dataset Visualization With One-Row Aggregation
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Solution Overview
Problem
Existing user interfaces for table join operations on large data sets present complex and confusing results due to repeated root object identifiers and large numbers of data rows, making it difficult for users to understand and analyze the data effectively.
Innovation Solution
A method and system that aggregates multiple rows of related data from one-to-many joins into a single aggregation row per root object, allowing for a one-to-one presentation in the user interface, with options for user-selected aggregation types and requiring data export after aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional user interfaces present table join outputs with repeated root object identifiers and multiple data rows, then complete data information is preserved, but the visualization becomes complex and confusing for users
Solution Approach 1:
The patent merges multiple rows of related attribute data corresponding to the same root object into a single aggregated row. This consolidation combines all relevant data while eliminating redundant root object identifiers, preserving complete information while simplifying visualization.
Solution Approach 2:
The patent transforms the data presentation from a vertical dimension (multiple rows per root object) to a horizontal dimension (multiple columns within a single row). This dimensional change allows complete data to be displayed in a compact, non-repetitive format that is easier for users to understand.
2Quantity of substance
If large volumes of data are processed through table join operations, then comprehensive analysis results are obtained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent performs aggregation computation as a preliminary action during the data processing stage, before final visualization. By pre-aggregating the joined data into consolidated rows with representative values, the system reduces the computational burden of subsequent operations and accelerates final result generation.
3Reliability
If multiple iterations of data exploration are performed, then thorough analysis is achieved, but cumulative time and resource consumption increases
Solution Approach 1:
The patent combines all necessary data for a given root object into a single aggregated row that contains comprehensive information. This consolidation allows users to perform thorough analysis in fewer iterations, as each aggregated row provides complete data representation without requiring repeated exploration of duplicate root objects.
4Loss of information
If detailed row-by-row data presentation is used, then complete data visibility is maintained, but the user interface complexity increases
Solution Approach 1:
The patent redistributes data from the vertical dimension (multiple rows) to the horizontal dimension (multiple columns within a single aggregated row). This transformation maintains complete data visibility while dramatically simplifying the user interface structure, eliminating the complexity associated with repeated root object identifiers and numerous data rows.
Data Source
AI summary
In some embodiments, systems and methods for visualizing one or more datasets include importing a plurality of root objects, each root object including linked data attributes and obtaining a joined dataset based on the plurality of root objects, that includes for each or the plurality of root objects, a plurality of rows of related attribute data linked to each root object as a result of a join operation. The systems and methods perform an aggregation computation on the plurality of rows of related attribute data corresponding to each of the plurality of root objects to produce a corresponding single aggregation row of consolidated data for each root object and present a user interface that shows each of the plurality of root objects with their corresponding single aggregation row of consolidated data resulting from the aggregation computation, in a one-to-one manner.