Relational Database Query Planning Using Analytical Data Units
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Solution Overview
Problem
Relational database management systems face inefficiencies in query processing due to the need to access large amounts of data, which can be costly in terms of time and memory, and lack advanced analytical capabilities to optimize query execution.
Innovation Solution
The implementation of a relational database management system that utilizes analytical information, such as rough set analysis, to minimize data access and optimize query execution by grouping data elements into units and using information units to plan and execute queries, allowing for the use of analytical information to determine the fastest approach to retrieve necessary data without accessing underlying data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional relational database systems access large amounts of data to resolve queries, then query accuracy is maintained, but query response time increases and memory consumption increases
Solution Approach 1:
The patent segments data into data units and separates analytical information from raw data. By organizing data into manageable units with associated metadata, the system can selectively access only relevant data units for query resolution rather than scanning entire tables, thereby reducing data access volume while maintaining query accuracy.
Solution Approach 2:
The patent pre-computes and stores analytical information (statistics, histograms, summaries) about data units before queries are executed. This preliminary action enables the query optimizer to make informed decisions about which data units to access and how to execute queries efficiently, reducing the need to access large amounts of raw data during query processing.
2Quantity of substance
If analytical information is stored separately from data units, then storage efficiency improves, but system complexity increases
Solution Approach 1:
The patent merges data units with their corresponding analytical information into integrated storage structures. Each data unit is associated with its analytical information through identifiers, creating a unified organization where related data and metadata are stored together. This approach improves storage efficiency by eliminating redundant data copies while managing complexity through systematic association mechanisms.
3Productivity
If rough set analysis is applied to minimize data access, then query execution speed improves, but computational overhead during analysis increases
Solution Approach 1:
The patent applies rough set analysis and other analytical techniques in advance to compute statistical information about data units. This preliminary computational effort creates reusable analytical summaries that can be rapidly queried without repeating the heavy computation. The upfront computational overhead is amortized across multiple queries, resulting in faster query execution speeds.
Solution Approach 2:
The patent creates analytical copies (statistics, histograms, summaries) of the actual data that capture essential characteristics without duplicating the full data volume. These analytical copies serve as proxies for rapid query evaluation, enabling fast query execution while avoiding the need to process the complete dataset for each query.
Data Source
AI summary
A system and method of processing a data query in a data processing system is provided. The data in the data processing system includes a plurality of individual data elements. The data elements are grouped and stored in at least one data unit. The information about the at least one data unit is gathered and stored in at least one information unit. The method comprises receiving the data query to be executed; using the information in the at least one information unit to optimize and execute the query; resolving the data query; and returning results of the data query for use by the data processing system.