Dynamic Data Reach Extension for Search Queries
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Solution Overview
Problem
Collaborative entities, such as users, devices, and bots, often face sub-optimal data reach issues due to dynamic constraints on accessible data sources, limiting their ability to retrieve relevant information for search requests.
Innovation Solution
A computer-implemented method that uses prior collaboration data to build a data reach model, determining new data sources and their duration of addition based on real-time entity activity, thereby extending the data reach for search requests by incorporating additional relevant data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities are constrained to access only their assigned data sources, then system security and access control are maintained, but data reach and search result completeness deteriorate
Solution Approach 1:
The system performs preliminary analysis of collaboration patterns and pre-identifies relevant data sources that entities should access. By analyzing historical collaboration data, the system proactively determines which additional data sources are relevant to current search requests, allowing entities to access expanded data sources without compromising security control.
Solution Approach 2:
The system introduces an intermediary mechanism (the data reach extension system) that mediates between security constraints and information needs. This intermediary analyzes collaboration patterns and search requests to dynamically determine appropriate data source access, balancing security requirements with the need for comprehensive information retrieval.
2Measurement precision
If entities access more data sources, then search result accuracy and statistical significance improve, but system complexity and access management difficulty increase
Solution Approach 1:
The system enables entities to automatically access relevant data sources without manual configuration or complex approval processes. By leveraging collaboration pattern analysis, the system self-determines which data sources should be accessible to each entity based on their search context and collaboration history, eliminating the need for manual access management while improving search accuracy.
Solution Approach 2:
The system dynamically changes access parameters based on real-time analysis of collaboration patterns and search requests. Instead of static access control lists, the system adjusts data source accessibility parameters dynamically, allowing entities to access additional data sources when relevant to their current search context, thereby improving accuracy without permanent complexity increases.
3Adaptability or versatility
If data reach is extended dynamically based on real-time activity, then relevance of accessed data improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of collaboration patterns during non-critical periods, building a knowledge base of entity relationships and data source relevance. This pre-computed information is then quickly applied during real-time search operations, allowing dynamic data reach extension without significant delays in processing search requests.
Solution Approach 2:
The system applies partial analysis to real-time search requests, focusing only on the most relevant collaboration patterns and data sources rather than performing complete analysis. By selectively extending data reach based on key indicators, the system achieves adaptability while minimizing additional processing time.
Data Source
AI summary
Provided are techniques for collaborative learned scoping to extend data reach for a search request. From monitoring prior collaboration data of entities discussing topics, the topics, access response times to data sources with content objects for the topics, and topic response content of the content objects are derived. A data reach model is built using the topics, the access response times, the topic response content, and data sources of the topics. For a topic of the topics, the data reach model is used to determine a new data source to be added and a duration of adding the new data source based on real time entity activity. The new data source is added to a data reach of the current group of entities. In response to receiving a search request, the search request is issued against the data sources and the new data source, and results are returned.