Interest-Driven Data Visualization Using Trellised Visualizations
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Solution Overview
Problem
Current business intelligence systems face challenges in efficiently generating and visualizing large datasets, particularly in providing real-time updates and interactive visualizations that allow users to explore and analyze data from multiple perspectives within interest-driven business intelligence systems.
Innovation Solution
The development of interest-driven data visualization systems that include processors and memory configured to store applications capable of defining reporting data requirements, generating data retrieval jobs, receiving aggregate data, creating reports, and generating visualization image data for display, utilizing trellised visualizations to interactively explore and update datasets in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional business intelligence systems are used to generate and visualize large datasets, then data storage and processing are achieved, but real-time updates and interactive visualizations from multiple perspectives are not provided efficiently
Solution Approach 1:
The patent segments the data visualization system into multiple independent components: data sources, processing logic, visualization generators, and display modules. This allows parallel processing of different data subsets and enables real-time updates without regenerating entire visualizations, thereby improving productivity while managing complexity through modular architecture.
Solution Approach 2:
The system pre-processes and pre-aggregates data from multiple sources before visualization is requested. Data is organized into standardized formats and stored in optimized structures ahead of time, enabling rapid report generation and real-time interactive visualizations without complex processing during query execution.
2Ease of operation
If comprehensive data analysis from multiple perspectives is enabled, then data accessibility and analysis efficiency are improved, but data retrieval and processing time increase
Solution Approach 1:
The patent introduces temporal and hierarchical dimensions to data retrieval. Data is pre-aggregated at multiple hierarchical levels and time intervals, allowing users to explore data from multiple perspectives (different granularities, time periods, categories) without retrieving raw data. This dimensional organization enables fast access to summarized views while maintaining the ability to drill down when needed.
Solution Approach 2:
The system replaces traditional mechanical data retrieval methods (sequential scanning, full table scans) with optimized data structures and indexing mechanisms. Data is stored in columnar formats with sophisticated indexes that enable rapid filtering, aggregation, and sorting operations, dramatically reducing data retrieval time while maintaining comprehensive analysis capabilities.
3Reliability
If real-time data updates are implemented, then data freshness and analysis relevance are improved, but system resource consumption and processing load increase
Solution Approach 1:
The system implements periodic data update cycles rather than continuous real-time processing. Data sources are polled at predetermined intervals, and visualizations are refreshed based on these periodic updates. This approach maintains data freshness and reliability while significantly reducing processing energy consumption compared to continuous real-time processing, as the system can enter low-power states between update cycles.
Data Source
AI summary
Systems and methods for interest-driven data visualization systems are illustrated. The system includes a processor and a memory configured to store an interest-driven data visualization application. The application configures the processor to obtain reporting data including a plurality of datasets from an interest-driven business intelligence server system; generate visualization rendering data for the datasets based on associations between visualization metadata and the datasets; and generate visualization image data based on the visualization rendering data. The visualization image data is displayable using and includes visual representations of at least a portion of the reporting data. Pieces of the visualization image data correspond to master and slave visualizations. The slave visualization is related to the master visualization such that input data associated with the master visualization is mapped to the slave visualization and at least one piece of input data associated with the slave visualization is not mapped to the master visualization.