Interest-Driven Data Visualization with Trellised Visualizations
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Solution Overview
Problem
Current business intelligence systems face challenges in efficiently visualizing and analyzing large volumes of structured, semi-structured, and unstructured data, particularly in providing real-time interactive reports and dynamic data exploration within interest-driven business intelligence systems.
Innovation Solution
The development of interest-driven data visualization systems that include a processor and memory configured to store a data visualization application, which defines reporting data requirements, generates data retrieval jobs, receives aggregate data from a business intelligence server, and creates visualizations using reporting data, visualization metadata, and display devices to render visual representations of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional business intelligence systems process and visualize large volumes of data, then data analysis capability is improved, but system complexity and processing time increase
Solution Approach 1:
The patent divides the data processing system into multiple independent components: a data storage layer for raw data, an aggregation layer for pre-computed statistics, and a visualization layer for rendering. This segmentation allows each component to handle specific portions of the data independently, reducing overall system complexity while maintaining the ability to process large volumes of data.
Solution Approach 2:
The system performs preliminary aggregation of data into summary statistics before visualization requests are made. By pre-computing aggregate data and storing it in an aggregation layer, the system avoids the need to process entire large datasets in real-time during visualization operations, thereby reducing processing time and system complexity during actual data exploration.
2Speed
If real-time interactive reports are generated from large datasets, then responsiveness is improved, but data processing time increases
Solution Approach 1:
The system pre-aggregates data into summary statistics and stores them in an aggregation layer before actual visualization requests. When users interact with the system, the pre-computed aggregate data is retrieved and visualized immediately, enabling real-time interactive reports without the need to process large datasets in real-time, thus maintaining both speed and responsiveness.
Solution Approach 2:
The aggregation layer acts as an intermediary between the raw data storage layer and the visualization layer. This intermediate layer contains pre-computed summary statistics that can be quickly retrieved and used to generate visualizations, eliminating the need to process raw data in real-time during interactive operations and thereby reducing data processing time while maintaining responsiveness.
3Adaptability or versatility
If dynamic data exploration is enabled, then analytical capability is improved, but system response time increases
Solution Approach 1:
The system pre-computes and stores aggregate data in an aggregation layer before dynamic exploration is needed. When users perform dynamic data exploration operations, the system retrieves pre-aggregated data and updates visualizations accordingly, enabling flexible and adaptive data exploration without the time penalty of processing raw data in real-time during each exploration operation.
Solution Approach 2:
The aggregation layer serves as an intermediary that stores pre-computed summary statistics, enabling dynamic data exploration operations to proceed quickly by retrieving and updating existing aggregate data rather than processing raw data from scratch. This intermediary layer maintains the system's adaptability to various exploration queries while minimizing response time.
4Ease of manufacture
If visualization metadata and reporting data are stored separately, then data organization is improved, but data retrieval complexity increases
Solution Approach 1:
The patent segments data into distinct organizational layers: a raw data storage layer, an aggregation layer for summary statistics, and a visualization layer for rendering. Metadata about data structure, relationships, and visualization parameters is organized separately in association with each layer. This segmentation improves data organization and maintainability while the layered architecture provides clear retrieval paths that reduce complexity despite the separation.
Data Source
AI summary
Systems and methods for interest-driven data visualization systems in accordance with embodiments of the invention are illustrated. In one embodiment, an interest-driven data visualization system includes a processor and a memory configured to store an interest-driven data visualization application, wherein the interest-driven data visualization application configures the processor to define reporting data requirements, generate data retrieval job data based on the at least one piece of reporting data metadata and the data description metadata, transmit the data retrieval job data to an interest-driven business intelligence server system, receive aggregate data from the interest-driven business intelligence server system, create at least one piece of reporting data using the received aggregate data, the data description metadata, and the reporting data metadata, generate a report using the at least one piece of reporting data, the reporting data requirements, and the visualization metadata, and generate visualization image data based on the generated report.