Object Model for Heterogeneous Data Visualization
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Solution Overview
Problem
Generating data visualizations that combine data from multiple tables is challenging, especially when dealing with complex or multiple fact tables, as existing methods often lack clarity on the type of visualization to generate based on user selections, and require expertise to construct an object model that can assist all users.
Innovation Solution
An object model is constructed to store relationships between data elements, allowing a data visualization application to leverage this information and assist users in generating visualizations by identifying reachable dimensions and measures, and rolling up values to specified levels of detail for aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If an object model is constructed to store relationships between data elements, then the data visualization application can assist users in generating visualizations by identifying reachable dimensions and measures, but the complexity of constructing and maintaining the object model increases
Solution Approach 1:
The patent applies preliminary action by constructing the object model in advance, storing relationships between data elements, dimensions, and measures before the user needs to generate visualizations. This pre-computed structure enables the system to quickly identify reachable dimensions and measures, assisting users in generating visualizations without requiring them to manually analyze complex data relationships at runtime.
2Adaptability or versatility
If the object model stores all relationships between data elements, then comprehensive visualization options are available, but the amount of data processing and memory required increases
Solution Approach 1:
The patent applies segmentation by dividing the object model into distinct components: data elements, dimensions, measures, and their relationships. This structured segmentation allows the system to efficiently query only the relevant portions of the model needed for a specific visualization task, rather than processing the entire data set. The hierarchical organization enables selective traversal from data elements to reachable dimensions and measures based on user selections.
3Loss of information
If multiple fact tables are combined to create comprehensive visualizations, then more complete data analysis is achieved, but the difficulty of determining what type of visualization to generate increases
Solution Approach 1:
The patent applies the intermediary principle by introducing the object model as a mediating layer between multiple fact tables and the visualization generation process. The object model stores and organizes relationships between data elements from different fact tables, dimensions, and measures in a unified structure. This intermediary representation simplifies the complexity of multiple fact tables by providing a standardized interface for querying reachable dimensions and measures, enabling the system to automatically determine appropriate visualization types based on user selections without requiring manual analysis of complex table relationships.
Data Source
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AI summary
The process receives a visual specification, which specifies data sources, visual variables, and data fields from the data sources. Each visual variable is associated with one or more data fields and each data field is either a dimension or a measure. For each measure m, the process identifies a set R(m) consisting of dimensions that are reachable from the measure by a sequence of many -to-one relationships in an object model for the data sources. For each distinct set R, the process forms a data field set S, consisting of each dimension in R and each measure m for which R(m) = R. For each set S and for each measure in the set S, the process aggregates values of the measure according to the dimensions in S. The process builds a data visualization according to the data fields in the set S and the visual variables they are associated with.