Query-Compliant Hashing for Database Storage Efficiency
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Solution Overview
Problem
Current database management systems face inefficiencies in storage and computational resources due to the need to store and process large amounts of data for various query requirements, particularly in handling match, disjunctive non-match, and conjunctive non-match field values.
Innovation Solution
The implementation of segmentation-based hashing models that generate hash representations for match, disjunctive non-match, and conjunctive non-match requirements, allowing for the combination of hash representations to reduce storage needs and simplify query processing by storing and retrieving data based on expected query structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional database storage methods are used to store all field values for query requirements, then query accuracy is maintained, but storage requirements and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the necessary hash representations for match requirements and disjunctive non-match requirements from the complete field value set. By taking out only the essential components needed for query evaluation, the system reduces storage requirements while maintaining query accuracy, as the extracted hash representations are sufficient for determining field value relationships without storing all original data.
Solution Approach 2:
The patent segments the query processing into distinct components: match requirement hash representations, disjunctive non-match requirement hash representations, and conjunctive non-match field value hash representations. This segmentation allows the system to store and process only the necessary portions of data separately, reducing overall storage requirements while maintaining the ability to accurately evaluate complex query conditions.
2Measurement precision
If all field values are stored and processed for query requirements, then query completeness is maintained, but computational resources and processing time increase
Solution Approach 1:
The system extracts and processes only the hash representations necessary for query evaluation rather than processing all original field values. This extraction approach maintains query completeness by preserving the essential information needed for accurate matching while significantly reducing computational resources required for processing.
Solution Approach 2:
The patent uses hash representations as copies of the original field values that preserve the necessary information for query evaluation. These hash copies can be processed more efficiently than the original data while maintaining query completeness, as the hash representations retain the essential characteristics needed for matching and filtering operations.
3Measurement precision
If detailed field value information is stored for all query requirements, then data accuracy is maintained, but storage efficiency decreases
Solution Approach 1:
The patent changes the representation parameter from storing actual field values to storing hash representations of field values. This parameter change maintains data accuracy by preserving the essential identifying characteristics through hashing, while significantly improving storage efficiency by reducing the amount of data that needs to be stored and managed.
Solution Approach 2:
The system extracts only the hash representations necessary for maintaining data accuracy in the context of query requirements. By taking out only the essential hash information rather than storing complete field values, the system maintains data accuracy for query evaluation while improving storage efficiency.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.