Dynamic Data Visualization Grouping for Large Sets
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Solution Overview
Problem
Current data display systems face challenges in handling large data sets, often becoming unresponsive or sacrificing visual richness and flexibility, as they assume uniformity in data items and performance across various data sources, leading to unsatisfactory user experiences.
Innovation Solution
The system dynamically renders visual representations of data sets by grouping items with common characteristics, retrieving a minimum necessary portion of data to display a visual identifier for each group, allowing a larger portion of the data set to be represented in a single view without losing visual richness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed number of results are displayed on a single page, then the display system maintains simple structure and fast response, but the user must scroll or page through hundreds of result pages to get a feel for the scope and breadth of results
Solution Approach 1:
The patent divides the data set into meaningful groups based on shared characteristics (e.g., file type, source, date). Each group is represented by a visual identifier that aggregates multiple items, allowing users to perceive the scope of results at a glance without scrolling through individual items. This segmentation transforms a flat list into a hierarchical structure that reveals data breadth efficiently.
Solution Approach 2:
The patent introduces visual grouping as an additional dimension for organizing data beyond simple sequential listing. By adding the dimension of categorical grouping with visual identifiers, the system enables users to comprehend the scope and diversity of results spatially rather than requiring linear traversal through hundreds of pages.
2Reliability
If the system retrieves and displays all data items to ensure comprehensive results, then complete data coverage is achieved, but the system becomes unresponsive when handling large data sets
Solution Approach 1:
The patent extracts and displays only the essential identifying characteristics of data groups rather than retrieving complete data for all items. By taking out just the key visual identifiers needed to represent each group (such as file type icons, source badges, or date ranges), the system maintains result comprehensiveness while dramatically reducing data retrieval and rendering time.
Solution Approach 2:
The patent implements partial action by retrieving and displaying a representative subset of data characteristics rather than complete data for all items. The visual identifiers provide sufficient information for users to assess result scope and make decisions, eliminating the need to process and display every single data item while maintaining system responsiveness.
3Device complexity
If the system assumes all data items will be displayed sequentially and are exactly the same size, then the display structure becomes simple and uniform, but visual richness and display flexibility are lost
Solution Approach 1:
The patent applies local quality by allowing different visual identifiers and grouping strategies for different types of data items based on their specific characteristics. Rather than enforcing uniform display across all items, the system adapts the visual representation to the local properties of each data group (e.g., different icons for different file types, varied grouping criteria), thereby achieving both structural organization and visual richness.
Solution Approach 2:
The patent introduces dynamic adaptability where the display structure automatically adjusts based on the characteristics of the data being displayed. The system can dynamically change grouping criteria, visual identifier styles, and layout configurations to suit different data types and user needs, transforming a static uniform display into a flexible, context-aware visualization system.
Data Source
AI summary
Computerized methods and systems for dynamically rendering visual representations of data sets are provided. Upon receiving a request for a particular data set (for instance, in response to receiving a search request), a data set is identified and a minimum portion thereof is retrieved that is necessary to render a visual representation of the data set. In this regard, items sharing a common characteristic may be grouped with one another so that only a single visual identifier of a group of data items may be displayed. In this way, a larger portion of the entire data set may be represented in a single view.