BI Search Indexing with Category Boost Values
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Solution Overview
Problem
Conventional Business Intelligence (BI) search engines inadequately rank search results based on the context and structure of BI artifacts, leading to irrelevant results due to a focus solely on term matching, which can mislead users and waste time.
Innovation Solution
A system generates a master index with index documents that map BI artifact contents to categories with default boost values, ranking search results by hit scores based on the number of hits, categories, and boost values, providing context-aware and meaningful rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional BI search engines rank artifacts based solely on term matching and number of terms matched, then the search process is simple and fast, but the search result relevance deteriorates and users are misled to wrong artifacts
Solution Approach 1:
The patent segments the search ranking process into multiple components: term matching, category identification, boost value assignment, and hit score calculation. Each BI artifact is divided into multiple categories (e.g., report, dashboard, KPI), and each category receives a specific boost value. This segmentation allows the system to consider contextual importance while maintaining a structured, manageable ranking framework.
Solution Approach 2:
The patent introduces boost values as a new parameter to modify the traditional term-matching ranking approach. By assigning different boost values to different categories and artifact types, the system dynamically adjusts the weight of term matches based on contextual importance. This parameter change transforms the ranking from a simple count-based system to a weighted scoring system that reflects business intelligence context.
2Measurement precision
If the search engine considers context and structure of BI artifacts in ranking, then search result accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements preliminary categorization of BI artifacts into predefined categories (report, dashboard, KPI, etc.) with assigned boost values. This preliminary action is performed during index creation, so that when a search query is executed, the system can directly apply the pre-established category weights without performing complex real-time analysis of artifact structures. This pre-computation significantly reduces search processing time while maintaining high ranking accuracy.
3Reliability
If multiple BI artifacts with different formats are indexed with the same weighting, then the indexing process is uniform and simple, but the search result quality deteriorates due to lack of differentiation
Solution Approach 1:
The patent applies local quality by assigning different boost values to different categories of BI artifacts based on their specific characteristics and importance. Instead of uniform weighting, each artifact type (report, dashboard, KPI) receives localized weight adjustments appropriate to its format and business value. This allows the indexing process to differentiate between artifact types while maintaining a systematic approach through predefined category schemas.
Data Source
AI summary
Systems and methods of searching by a processor are described herein. In accordance with one aspect of the present disclosure, a master index having a plurality of index documents corresponding to artifacts stored in a repository is generated. An index document maps contents from a source artifact to index categories having different default boost values indicating relative importance of the index categories. A search query is fired by a user to search the master index. A result list of artifacts having contents matching (hit) the search query is generated. Artifacts are ranked in the list based on hit scores according to number of hits, which categories the hits occurred and the default boost values of the index categories.