Query History Consolidation via Extraction and Merging
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Solution Overview
Problem
Current querying systems lack an efficient method to maintain and utilize the history of queries and their results, which limits insights into user behavior and patterns, and does not effectively consolidate or filter this data for improved user experiences and business decisions.
Innovation Solution
A system and method that involves receiving and storing queries and their results, along with context information, filtering and consolidating this data to reduce redundancy, and using modules to manage query data, including a query module, storage module, filter module, and consolidation module to track query frequency and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If query data and results are stored without filtering or consolidation, then complete query history is maintained, but data redundancy increases and storage efficiency decreases
Solution Approach 1:
The patent extracts and separates query data into distinct categories (exact matches, partial matches, no matches) and only stores representative samples of each category rather than all individual query-result pairs. This extraction approach maintains comprehensive query history information while reducing redundant storage of identical or similar queries.
Solution Approach 2:
The patent merges multiple similar query variations into consolidated categories. Instead of storing each unique query string separately, similar queries are grouped together and stored as a single representative entry with aggregated statistics, reducing the total quantity of stored data while preserving historical information.
2Loss of information
If all query data is stored for analysis, then user behavior insights are comprehensive, but storage resources are consumed excessively
Solution Approach 1:
The system extracts only the essential features needed for user behavior analysis (query categories, match types, temporal patterns) rather than storing complete query-result datasets. This extraction enables comprehensive behavioral insights while minimizing storage resource consumption.
Solution Approach 2:
The patent transforms detailed query data into aggregated statistical parameters (frequency counts, temporal distributions, category distributions). This parameter transformation maintains analytical value for understanding user behavior while dramatically reducing the volume of data requiring storage.
3Loss of information
If query data is stored without consolidation, then individual query results are preserved, but processing and analysis efficiency decrease
Solution Approach 1:
The system extracts and stores only the essential query characteristics and result patterns rather than complete individual query-result datasets. This extraction preserves the analytical value of individual queries while significantly improving processing efficiency through reduced data volume.
Solution Approach 2:
The patent performs preliminary consolidation and categorization of query data during the storage process itself, rather than storing raw data and consolidating later. This preliminary action reduces the complexity of subsequent processing operations and improves overall system productivity.
Data Source
AI summary
A method and system for maintaining history of query results is provided. In one aspect, a query and corresponding results retrieved for providing to the requestor are intercepted and saved. Related information about the query and the results are gathered and saved with the query and the results. The saved information may be filtered, consolidated, or otherwise processed and stored in a query storage. The saved information may be queried and be the basis of information from which results to other queries are obtained.