Search Device Relational Data Expansion Index
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Solution Overview
Problem
Conventional search devices face challenges in performing flexible fuzzy searches on embedded systems like car navigation apparatuses due to high data volume and processing time issues, especially when handling relationships between correlated key words, which leads to inefficient search results and prolonged processing times.
Innovation Solution
A search device is designed with a character-string search index and a relational-data expansion index, where the search query is divided into partial units, and the similarity is calculated considering the relationships between document data, using a weight-based correction mechanism to arrange results effectively, thereby reducing data volume and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indexes are prepared for every pattern of correlated key words (e.g., artist name, album name, song title), then search result relevance is improved, but data volume becomes enormous and processing time increases
Solution Approach 1:
The patent segments the search system into two distinct index structures: a character-string search index for basic fuzzy matching and a relational-data expansion index for relationship-based expansion. This segmentation allows each index to have a specific, limited function rather than storing all possible search patterns, thereby reducing overall data volume while maintaining search relevance.
Solution Approach 2:
The patent introduces a new dimension of relationship types (e.g., artist-album, artist-song, album-song) as a classification layer. Instead of creating separate indexes for every possible search pattern combination, the system uses relationship types to organize and expand search results dynamically, reducing data redundancy.
2Measurement precision
If indexes are prepared for every pattern of correlated key words, then search result relevance is improved, but processing time becomes excessively long
Solution Approach 1:
The patent divides the search process into two stages: first performing a quick character-string search to retrieve candidate documents, then selectively expanding results using the relational-data expansion index. This segmentation avoids processing all relationship data for every search query, significantly reducing processing time while maintaining relevance.
Solution Approach 2:
The patent applies partial action by performing relationship-based expansion only on the top-ranked candidate results from the character-string search, rather than expanding all possible matches. This selective approach reduces processing time while still capturing the most relevant results.
3Adaptability or versatility
If fuzzy search is performed using partial character-strings, then search flexibility is improved, but search precision deteriorates due to multiple partial matches
Solution Approach 1:
The patent implements a feedback mechanism where the character-string search results serve as input to the relational-data expansion process. The expansion step uses the relationship information to filter and re-rank the fuzzy match results, providing feedback that refines the initial broad matches into more precise final results.
Solution Approach 2:
The relational-data expansion index acts as an intermediary between the flexible but imprecise character-string search and the final search results. It mediates by adding relationship-based context to the fuzzy matches, improving precision without sacrificing the flexibility of partial character-string matching.
Data Source
AI summary
A search unit 60 calculates a character-string similarity by acquiring expansion source document data partially matching a search query 70 from a character-string search index 40, determines a relational-data expansion similarity by acquiring expansion destination document data defined to have a relationship with the expansion source document data from a relational-data expansion index 50 and correcting the acquired data by the character-string similarity of the expansion destination document data and relation weight data 80, and outputs a search result 90 arranged in an order of the relational-data expansion similarity.