Multi-database Document Search System with Supplemental Indexing
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Solution Overview
Problem
As the amount of data in databases increases, search operations become progressively slower, with existing technologies facing challenges in maintaining efficient search times and accuracy due to the buildup of indexes and frequent modifications, leading to stale indices and incomplete or inaccurate search results.
Innovation Solution
A system comprising a document intake system, a first database for storing objects and links, a search system for building an index, a second database for logging changes, and a document interface system that performs supplemental searches to ensure accurate and efficient retrieval of data, using scalable search techniques and ontology templates to generate objects and links, and updates the index periodically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in a database and searches are performed on the data, then data retrieval functionality is provided, but as the amount of data increases, search operations become progressively slower
Solution Approach 1:
The patent segments the database into multiple partitions or shards, distributing data across different storage units. Search operations are divided into parallel searches across these segments, reducing the time complexity from O(n) to O(n/p) where p is the number of partitions, thereby maintaining search speed as data quantity increases
Solution Approach 2:
The system pre-computes and stores indexes, summaries, and metadata about the data before search operations occur. This preliminary organization allows search operations to query pre-processed structures rather than scanning raw data, significantly reducing search time even as data volume grows
2Speed
If indexes are built to speed up searches, then search performance is improved, but indexes become stale and search results become incomplete or inaccurate when data modifications occur
Solution Approach 1:
The patent implements dynamic index update mechanisms where indexes are automatically updated when data modifications occur. The system uses change data capture and incremental update strategies to keep indexes synchronized with the underlying data, ensuring search result accuracy while maintaining performance
Solution Approach 2:
The system employs periodic index refresh operations and versioning strategies where indexes are rebuilt or updated at scheduled intervals or triggered by modification thresholds. This periodic maintenance ensures index freshness while balancing the overhead of index updates against search performance requirements
3Measurement precision
If processing operations are performed on vast amounts of data, then comprehensive data analysis is achieved, but processing time becomes unacceptably long
Solution Approach 1:
The patent implements approximate query processing and sampling techniques that provide sufficiently accurate results without processing the entire dataset. For certain query types, the system uses statistical sampling, approximate algorithms, or pre-computed aggregations that deliver acceptable precision with significantly reduced processing time
Solution Approach 2:
The system replaces traditional mechanical data processing approaches with parallel and distributed computing architectures. By using multiple processors, GPUs, or distributed node clusters to perform data analysis operations simultaneously, the system achieves comprehensive analysis of vast datasets without unacceptably long processing times
Data Source
AI summary
A document review system is configured to quickly provide search results for large amounts of documents. Ontology templates are used to generate objects and links based on the documents. Users can view documents, and linked objects and object properties can be displayed. An index assisted search can be performed to determine the linked objects and object properties. A supplemental search can be performed on a second database that tracks modifications to the documents or objects. The modifications in the second database can be periodically written to the first database, and the index can be rebuilt. The user experience can be free of slow searches and slow index rebuilds.