Subset Search Operators for Predictable Multi-Term Querying
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search engines face challenges in efficiently and predictably selecting search results, particularly in contexts like litigation discovery, due to the complexity of relevance scoring and the resource-intensive nature of manual query construction, which can be difficult to defend and may exceed available computing resources.
Innovation Solution
Implementing a subset search operator that allows simple specification of searches by combining results from a set of term processes, using a subset process that requires only one term process per search term, thereby optimizing the search tree level execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relevance scoring with threshold is used to select search results, then the search can handle large result sets and provide statistical effectiveness, but the user cannot predict or understand the relevance score and the search is difficult to defend
Solution Approach 1:
The search query is divided into multiple independent term processes, each handling one search term. The results from these segmented processes are then combined by the subset process, making the search mechanism transparent and predictable while maintaining efficiency.
Solution Approach 2:
The subset process acts as an intermediary that combines results from multiple term processes. This intermediary layer provides predictability by explicitly showing which terms match and how many matches are required, making the search defensible while maintaining statistical effectiveness.
2Manufacturing precision
If manual query construction is used to specify complex search criteria, then the search can be precise and defensible, but the query construction becomes time-consuming and resource-intensive
Solution Approach 1:
The search system performs self-service by automatically constructing the query structure. The user simply provides the search terms and the system automatically creates the appropriate term processes and combines them using the subset process, eliminating the need for manual query construction while maintaining precision.
Solution Approach 2:
The system changes the parameters of query construction from manual specification to automated generation. The subset process automatically determines the logical structure and combination methods, reducing query construction time while maintaining search precision through explicit parameter control.
3Manufacturing precision
If traditional search operators are used to find objects with specific terms, then the search can be precise, but the query structure becomes complex and exceeds available computing resources
Solution Approach 1:
The query structure is segmented into independent term processes, each handling a single search term. This segmentation simplifies the overall query structure by breaking down complex logical operations into manageable components that can be processed independently and combined efficiently.
Solution Approach 2:
Multiple term processes are merged by the subset process into a unified search result. This merging approach simplifies the query structure by using a single process to combine results from multiple terms, reducing computational complexity while maintaining precision.
Data Source
AI summary
Embodiments as disclosed may provide a search operator that addresses the problem of query construction for finding objects that include some number (N) of a set of terms (M). Moreover, embodiments as disclosed herein may efficiently implement the searches specified by such subset operators using a subset process that combines results from a set of term processes where only one term process may be needed for each search term (or search term variant).