Multi-Fact Data Visualization Queries for Accurate Measure Aggregation
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Solution Overview
Problem
Existing data visualization applications struggle with generating accurate visualizations from complex or multiple data sources, often leading to omitted data rows, overstated totals, and unclear data types, especially when constructing queries from multiple fact tables.
Innovation Solution
An object model is constructed to represent the relationships between data elements, allowing the data visualization application to recommend additional fields, limit actions, and generate accurate visualizations by leveraging the object model to handle complex data sources and multiple data sources, using a tree of logical tables and subqueries to ensure proper aggregation and representation of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single Level of Detail is specified for data visualization, then the visualization can be generated, but complex calculations cannot be built accurately
Solution Approach 1:
The patent segments the data model into multiple hierarchical levels (fact tables, dimension tables, and virtual tables) that can be independently configured. Each level can have its own Level of Detail specifications, allowing complex calculations to be built by combining multiple segmented levels rather than relying on a single LOD setting.
2Quantity of substance
If data is aggregated from multiple fact tables, then comprehensive data coverage is achieved, but data rows may be omitted or totals may be overstated
Solution Approach 1:
The patent introduces virtual tables as intermediary structures between multiple fact tables and the final visualization. These virtual tables contain pre-computed aggregated data with proper join keys and relationship definitions, serving as mediators that ensure accurate data combination without duplication or omission when multiple fact tables are aggregated.
Solution Approach 2:
The patent performs preliminary aggregation and validation of data from multiple fact tables by creating virtual tables before the final visualization is generated. This preliminary action includes establishing proper join relationships and computing aggregates in advance, preventing accuracy issues during the final query execution.
3Measurement precision
If an object model is constructed to represent data relationships, then accurate visualizations can be generated, but the system complexity increases
Solution Approach 1:
The patent creates a universal object model framework that handles multiple data sources, fact tables, and dimension tables through a single standardized structure. The virtual table concept provides a multi-functional interface that works across different data sources and calculation types, reducing the need for separate complex models for each scenario.
Data Source
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AI summary
A computer receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with either data fields (e.g., dimension and/or measures) or filters. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields, each of which corresponds to either a data field or a calculation that spans logical tables. The computer generates a dimension subquery for the dimensions and the filters. The computer also generates, for each measure, an aggregated measure sub query grouped by the dimensions. The computer forms a final query by joining the dimension sub query to each of the aggregated measure subqueries. The computer subsequently executes the final query and displays a data visualization according to the results of the final query.