Context-Aware Data Object Relevance System
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Solution Overview
Problem
Existing data access methods, such as keyword-based and content-based search tools, struggle to efficiently find relevant information, especially when it is contextually related but not directly similar, leading to difficulties in retrieving all relevant data objects associated with a specific task or activity.
Innovation Solution
A system that automatically collects and analyzes usage, location, and content evidence between data objects to determine relevance, storing this evidence for selecting and presenting relevant data objects to the user, thereby facilitating proactive information retrieval and display in a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search tools are used to find information, then users can search for specific terms, but users must explicitly type search terms which they may not remember or be aware of, making the process time-consuming
Solution Approach 1:
The system performs preliminary analysis of usage patterns, location data, and content relationships between data objects before the user needs to search. By pre-computing relevance evidence and organizing data objects based on their contextual relationships, the system eliminates the need for users to manually type search terms, directly resolving the contradiction between search accuracy and time consumption
Solution Approach 2:
The system automatically collects information about data object usage and relationships without requiring user input. By self-organizing data objects based on usage patterns and contextual relationships, the system provides proactive information retrieval that does not depend on user-initiated keyword searches, thereby reducing both time and effort required for information retrieval
2Measurement precision
If content-based matching approaches are used to find similar documents, then documents with similar content can be found, but documents with different content that are highly relevant cannot be easily found
Solution Approach 1:
The system moves beyond single-dimension content matching by incorporating multiple dimensions of analysis including usage patterns, location relationships, and contextual associations. This multi-dimensional approach allows the system to identify relevant data objects that may have different content but share usage patterns or contextual relationships, thereby resolving the contradiction between content matching precision and relevance detection versatility
Solution Approach 2:
The system creates a universal relevance determination mechanism that works across different types of data objects and relationship types. By collecting and analyzing multiple types of evidence (usage, location, content) that can apply to various data object pairs, the system achieves both precise content matching and versatile relevance detection for different contexts
3Measurement precision
If traditional search approaches are used to find relevant information, then individual data objects can be found, but groups of information relevant to a specific context cannot be recovered as a unit
Solution Approach 1:
The system merges multiple data objects that are relevant to a specific context into unified groups based on collected usage and location evidence. By combining related data objects into contextual groups rather than treating them as individual items, the system enables users to access entire relevant information sets as units, thereby improving both retrieval accuracy and access efficiency
Solution Approach 2:
The system performs preliminary organization of data objects into contextual groups based on usage patterns and relationships before users need to access them. By pre-grouping related data objects, the system eliminates the need for users to manually assemble multiple pieces of information, directly improving information access efficiency while maintaining retrieval accuracy
Data Source
AI summary
Systems and methods are provided to facilitate a user's access to data objects. According to some embodiments, information associated with use of data objects is automatically collected. The collected information may be analyzed to determine relevance evidence between data objects, and the relevance evidence may be stored. A first data object of interest is determined, and, based on the stored relevance evidence, a second data object associated with the first data object is selected. An indication of the second data object may then be provided.