Algebraic Data Model for Query Optimization
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Solution Overview
Problem
Conventional database systems are limited by predefined schema and structures that do not optimize for data storage and retrieval, leading to inefficiencies in query processing and data access, especially with disparate data formats like CSV and XML.
Innovation Solution
The system employs a data model based on extended set theory for storing and processing data, using algebraic relations to optimize storage and access, allowing for the seamless translation between formats and maintaining algebraic integrity, temporal invariance, and adaptive data restructuring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional database systems use predefined schema and structures, then data storage and retrieval can be organized, but query processing efficiency deteriorates due to non-optimized data access paths
Solution Approach 1:
The patent implements dynamic data structures that adapt to query patterns rather than using static predefined schemas. The system automatically reorganizes data storage and access paths based on actual query workloads, allowing the database structure to evolve and optimize itself over time, thereby improving query processing efficiency without manual intervention
Solution Approach 2:
The system dynamically changes storage and access parameters based on query analysis. By monitoring query patterns and adjusting data organization parameters accordingly, the system optimizes access paths for different query types, resolving the contradiction between structured storage and query efficiency
2Adaptability or versatility
If predefined relationships are imposed on data, then database structure can be maintained, but adaptability to different data formats deteriorates
Solution Approach 1:
The patent creates a universal data model that can handle multiple data formats (CSV, XML, JSON, etc.) through a common algebraic framework. This universal model eliminates the need for format-specific preprocessing and allows the same query mechanisms to work across diverse data sources, greatly improving adaptability while reducing structural complexity
Solution Approach 2:
The system introduces an intermediary algebraic data model that sits between raw data formats and query processing. This intermediary layer translates various data formats into a unified algebraic representation, enabling flexible handling of different formats without imposing rigid predefined relationships
3Productivity
If tight coupling between relationships and database structure is maintained, then data integrity can be ensured, but efficiency in capturing and processing diverse data formats deteriorates
Solution Approach 1:
The patent segments the data processing system into independent modular components: data ingestion modules, algebraic transformation modules, and query execution modules. Each module operates independently with well-defined interfaces, allowing efficient processing of diverse formats while maintaining data integrity through structured algebraic operations that preserve relational constraints
4Loss of time
If multiple storage accesses are required for a single query, then comprehensive data retrieval can be achieved, but access time increases due to storage speed limitations
Solution Approach 1:
The system performs preliminary analysis of query requirements and pre-loads or caches necessary data subsets before actual query execution. By anticipating data access patterns and preparing data in advance, the system reduces the number of actual storage accesses needed during query execution, thereby minimizing access time while maintaining comprehensive retrieval capability
Solution Approach 2:
The patent implements nested data structures where frequently accessed data elements are embedded within larger data containers. This nesting allows the system to retrieve only the necessary nested elements without accessing entire parent structures, significantly reducing storage access time while still providing comprehensive data retrieval when needed
Data Source
AI summary
Systems and methods for storing and accessing data. A query language statement may reference a plurality of data sets and a plurality of algebraic relations between the data sets may be composed from the query language statement. The algebraic relations may be used to define new data sets and to optimize access to data sets. A store of algebraic relations may be accumulated over time. Alternative collections of algebraic relations may be generated and evaluated to determine an optimized collection of algebraic relations to use in calculating and providing a requested data set. The optimization may be performed using the algebraic relations rather than retrieving underlying data sets from storage. As a result, optimization may be performed at processor speeds to minimize the amount of time required for data to be retrieved from slower storage.