Temporal Data Visualization Using Record Change Classification
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Solution Overview
Problem
Conventional visualization tools often focus on snapshots in time, failing to effectively represent changes across time periods, making it difficult for organizations to utilize their vast collections of temporal data to improve business practices.
Innovation Solution
A system and method for generating visualizations that represent changes over time by comparing data sets at different time points, classifying records as new, present, or removed, and using Sankey diagrams to depict value changes, with interactive controls for selecting time points and filtering data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional visualization tools are used to display data, then current/latest values can be shown, but changes across time periods cannot be effectively represented
Solution Approach 1:
The patent introduces a temporal dimension to traditional visualizations by implementing time-point selectors and change detection mechanisms. The system compares data across multiple time points (e.g., T1, T2, T3) and visualizes changes in a new dimension, showing what has been added, removed, or modified between time periods. This resolves the contradiction by preserving temporal information without fundamentally redesigning the entire visualization system.
Solution Approach 2:
The visualization system segments temporal data into discrete time points and change categories. Records are divided into three categories: added records (present in current time point but not in previous), removed records (present in previous but not in current), and modified records (present in both but with changed values). This segmentation allows temporal information to be preserved and visualized effectively while maintaining system manageability.
2Quantity of substance
If vast collections of temporal data are collected, then more information is available, but it becomes difficult to effectively utilize the data
Solution Approach 1:
The system extracts only the relevant temporal changes from vast data collections rather than processing entire datasets. By focusing on what has changed between time points (additions, removals, modifications), the system makes large volumes of temporal data manageable and useful. Change detection queries extract only the necessary information for visualization, improving ease of operation while preserving data quantity.
Solution Approach 2:
Instead of processing all temporal data completely, the system performs partial action by focusing on change detection for specific records and time periods. Users can select particular time points and filter for specific types of changes, allowing them to utilize data effectively without being overwhelmed by its full volume. This selective approach makes data utilization easier while maintaining access to the complete data collection.
Data Source
AI summary
A user selects a data source. The user also selects a first point in time and a second point in time. The visualization platform generates a query to perform a full outer join between (1) a first data set corresponding to the first point in time and (2) a second data set corresponding to the second point in time. The platform then executes the query to retrieve a result set. The platform classifies each record in the retrieved result set as new, present, or removed according to whether the respective record: (1) corresponds only to a record in the second data set; (2) corresponds to records in both the first data set and the second data set; or (3) corresponds only to a record in the first data set. Finally, the platform generates and displays a Sankey diagram or Waterfall chart according to the retrieved result set and the classifications.