Query-Based Isolator for Minimal Database Subset Extraction
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Solution Overview
Problem
Databases have grown exponentially, leading to inefficiencies due to the unnecessary use of resources (time, disk space, and memory) for executing queries, as there is no automated approach to extract a minimal subset of the database for specific queries, and dependencies between queries are not readily available for parallelization.
Innovation Solution
A system comprising an observer, collator, and extractor to identify and extract a database subset for queries, reducing unnecessary data and enabling parallel execution on smaller subsets, thereby saving resources and improving query efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire database is used to execute queries, then query results are complete and accurate, but resource consumption (time, disk space, memory) increases unnecessarily
Solution Approach 1:
The system segments the database into a complete database and a subset database containing only the minimal records needed for specific queries. The query processing system selectively accesses either the complete database or the subset database depending on the query type, thereby reducing resource consumption while maintaining query result accuracy for applicable queries.
Solution Approach 2:
The system extracts a minimal subset of records from the complete database that are necessary to answer specific queries. This extracted subset is stored separately and used for those specific queries, eliminating the need to process the entire database and thereby reducing resource consumption while preserving the ability to obtain accurate results.
2Loss of energy
If a minimal subset of the database is extracted for specific queries, then resource efficiency improves, but automated extraction and dependency identification are not readily available
Solution Approach 1:
The system employs a feedback mechanism where the query processing system monitors query patterns and record access patterns, automatically identifying which records are frequently accessed together. This feedback information is used to automatically construct and update the subset database, enabling automated extraction without manual intervention while improving resource efficiency.
Solution Approach 2:
The system performs preliminary analysis of query patterns and record dependencies before executing actual queries. By pre-identifying and extracting the minimal subset of records needed for specific query types, the system prepares the subset database in advance, enabling efficient automated extraction and reducing resource consumption during query execution.
3Productivity
If the database is divided into smaller subsets for parallel processing, then query execution speed improves, but database complexity and management difficulty increase
Solution Approach 1:
The system segments the database into a complete database and a subset database, where the subset database contains only the minimal records needed for specific queries. This segmentation enables parallel processing by allowing the query system to independently access the subset database for certain queries without locking or coordinating with the complete database, thereby improving query execution speed while keeping management complexity low through clear separation of concerns.
Data Source
AI summary
A query-based isolator comprising an observer, a collator and an extractor. The isolator reduces the database on which the query is run, to a new smaller, database on which the query can be performed, with the same result obtained on the smaller database, as that obtained on the original database.