Database Record Matching via Dynamic Optional Element Association
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Solution Overview
Problem
Current database systems face inefficiencies in manually augmenting records with optional elements to increase the likelihood of matching search requests, leading to increased storage and processing demands without commensurate performance improvements.
Innovation Solution
A query system dynamically associates optional elements with database records based on historical data and search requests, automatically selecting and adjusting features to enhance matching likelihood, reducing storage and processing burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually augment database records with diverse optional features to increase matching likelihood, then the likelihood of records satisfying search requests improves, but storage requirements and processing load increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing compatibility information between optional elements and required elements during record creation. This allows the query system to quickly determine matching likelihood without manually augmenting records with all possible optional features, thereby reducing storage requirements while maintaining reliability.
Solution Approach 2:
The query system automatically determines which optional elements to associate with a search request based on compatibility information, eliminating the need for manual augmentation by users. The system serves itself by autonomously selecting appropriate optional elements, reducing both storage requirements and processing load.
2Reliability
If users manually augment database records with diverse optional features, then the likelihood of records satisfying search requests improves, but processing load increases
Solution Approach 1:
Compatibility information between optional and required elements is pre-computed and stored during record creation, rather than being calculated in real-time during query processing. This preliminary action significantly reduces the processing load during search operations while maintaining the ability to accurately determine matching likelihood.
Solution Approach 2:
The query system autonomously selects optional elements based on pre-stored compatibility information, eliminating the need for manual user input and reducing processing overhead. The system efficiently determines matching likelihood through automated comparison of search criteria against pre-computed compatibility data.
3Reliability
If the query system associates optional elements with database records, then the likelihood of matching search requests improves, but duplicate records increase
Solution Approach 1:
A single database record can serve multiple search requests by associating it with multiple optional elements based on compatibility information. Instead of creating duplicate records for different search scenarios, the system makes one record universally applicable to various search criteria through dynamic association of appropriate optional elements.
Solution Approach 2:
The association between optional elements and database records is dynamic rather than static. The query system determines which optional elements to associate based on the specific search request, allowing the same record to adapt to different search contexts without requiring duplicate static versions for each possible search scenario.
4Manufacturing precision
If users manually augment records with optional elements, then record generation completeness improves, but time and effort required increases
Solution Approach 1:
The query system automatically determines which optional elements to associate with database records based on search requests and pre-stored compatibility information, eliminating the need for manual user input. This self-service approach ensures complete and accurate record augmentation without consuming user time or effort.
Solution Approach 2:
Compatibility information is pre-computed and stored during record creation, enabling the query system to automatically and efficiently determine appropriate optional elements during search operations. This preliminary preparation eliminates the need for time-consuming manual augmentation while ensuring complete and accurate record processing.
Data Source
AI summary
A system includes one or more processors and a non-transitory machine-readable storage device comprising processor executable instructions that, when executed, cause the one or more processors to perform a set of operations comprising receiving a search request for one or more database records of a network based publication system, and identifying a first database record corresponding to at least a portion of the search request, where the database record having a first element. The set of operations also include selecting a second element of a second database record based on the first database record and adjusting the first element of the database record based on the second element. The set of operations then includes generating a formatted user interface comprising a representation of the database record and the adjusted first element, and causing, in response to the search request, presentation of the formatted user interface on the remote computing device.