Level of Detail Expressions for Visual Analytics
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Solution Overview
Problem
Current data visualization systems lack the ability to effectively support interactive visual analysis, particularly when dealing with large or complex data sets, as they often fail to provide adequate tools for computing values at multiple levels of detail and integrating analytic functions.
Innovation Solution
The implementation introduces Level of Detail (LOD) expressions that allow users to specify, create, and modify LOD expressions through intuitive graphical user interfaces, enabling support for analytic functions like Rank and Running Sum, and allows for multi-step calculations at different levels of detail, translating these expressions into executable database queries for data visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LOD expressions are extended to support analytic functions for multi-level calculations, then data analysis capability is improved, but system complexity increases
Solution Approach 1:
The system segments the calculation process into distinct levels (data source level and visualization level) using LOD expressions. Each level can independently specify aggregation granularity, allowing complex analytics to be broken down into manageable computational stages that can be processed separately and combined.
Solution Approach 2:
LOD expressions serve as an intermediary mechanism between the data source and visualization layers. They provide a formal syntax that mediates the translation of user intent into executable database queries, bridging the gap between high-level analytical requirements and low-level data processing operations.
2Measurement precision
If multiple aggregation levels are computed, then computational precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary aggregation computations at the data source level before data is transferred to the visualization layer. By pre-computing aggregates at different granularity levels and storing them in the data model, the system reduces the computational burden during visualization rendering, achieving both precision and performance.
3Manufacturing precision
If granular control over computations is enabled, then analytical accuracy is improved, but ease of operation decreases
Solution Approach 1:
The system enables users to self-configure the desired level of aggregation and computation granularity through the LOD expression syntax. Users can independently specify which dimensions to aggregate by, at what level of detail, and which analytic functions to apply, giving them direct control over the analytical process without requiring complex configuration interfaces.
Data Source
AI summary
A computing device receives user selection of a data source. The computing device receives a user input to specify a level of detail (LOD) expression, which includes a first keyword, a SORT keyword, and an analytic expression. In response to the user input, the computing device identifies one or more data fields from the data source. The device translates the LOD expression into one or more executable database queries referencing the identified data fields. The computing device executes the queries to retrieve data from the data source. The computing device generates and displays a data visualization using the retrieved data.