Dynamic Visual Bundlers for Large Dataset Interaction
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Solution Overview
Problem
Conventional data visualization systems struggle with efficiently visualizing and interacting with very large datasets, as they often rely on predefined queries and data paths, limiting user flexibility and increasing wait times.
Innovation Solution
A system that includes a request handler, property filter, template selector, and layout engine to generate visual bundlers based on user requests, allowing for dynamic filtering and geometric representation of data subsets, enabling seamless zooming and intuitive interaction with large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data visualization systems use predefined queries and data paths, then the system structure is simplified and easier to implement, but user flexibility and adaptability are reduced
Solution Approach 1:
The system dynamically generates visualizations based on user requests rather than using fixed predefined queries. The visualization engine receives user requests, filters data elements based on requested properties, selects appropriate layout templates, and generates visual bundlers on-demand, allowing flexible adaptation to different user needs while maintaining a consistent system architecture.
Solution Approach 2:
The system segments the data visualization process into distinct modular components: request handler, property filter, template selector, and layout engine. Each component handles a specific aspect of visualization generation, allowing the system to manage complexity through division of labor while providing flexible user interactions through coordinated operation of these modules.
2Measurement precision
If conventional systems provide simplified grouping of data elements, then the visualization is easier to generate and display, but the level of detail and measurement precision is reduced
Solution Approach 1:
The system pre-processes and filters data elements based on requested properties before visualization generation. The property filter component prepares data subsets in advance according to the requested properties, and the template selector pre-identifies appropriate layout templates, enabling fast visualization generation while maintaining high detail levels through pre-prepared data structures and templates.
Solution Approach 2:
The system changes parameters such as data filtering criteria, layout template selections, and visual bundler configurations based on user requests. By dynamically adjusting these parameters rather than using fixed simplified groupings, the system can deliver high-level detail visualizations while maintaining efficient generation speeds through parameterized processing.
3Adaptability or versatility
If the system generates visual bundlers dynamically based on user requests, then user flexibility and detail level are improved, but the processing time and wait time increase
Solution Approach 1:
The system performs preliminary data filtering and template selection based on user requests before final visualization generation. The property filter and template selector operate in advance to prepare data subsets and identify appropriate layouts, reducing the time required for final visualization rendering and minimizing user wait time while maintaining flexible, detailed visualizations.
4Measurement precision
If the system provides detailed visualization of large datasets, then the measurement precision and level of detail are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system segments the data processing and visualization into distinct modular components: request handler, property filter, template selector, and layout engine. Each component handles specific computational tasks independently, managing overall computational complexity through division of labor while enabling detailed visualizations through coordinated processing of data subsets and layout templates.
Solution Approach 2:
The system applies different processing qualities and detail levels to different parts of the data based on user requests and data importance. By focusing computational resources on specific data elements and properties that users care about rather than uniformly processing all data, the system achieves high measurement precision for relevant details while reducing overall computational complexity.
Data Source
AI summary
Techniques are described for providing visual bundlers that group and represent specified data subsets of very large datasets in a manner that is expressive and intuitive for a user, and which provide a dynamic, configurable visualization that may be leveraged by the user to search, aggregate, or otherwise interact with the data of a very large dataset. By nesting such visual bundlers, a geometric equivalent of a dynamically-generated search tree is realized, in which a structure for executing a search for a desired data element is not required to be fixed or otherwise predefined. Consequently, during a runtime of providing related visualizations, multiple alternative search trees may be created for the same underlying content, with minimal effort, and in a manner that is highly intuitive and useful for the user.