Database Compression Using Frequent Attribute-Value Coding
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Solution Overview
Problem
Database performance is hindered by slow disk access times in hard disk drives, and while solid state drives improve this, they are expensive and inefficient due to increasing data storage needs.
Innovation Solution
A database compression system that analyzes a database schema to identify frequent attribute/value pairs, assigning condensed codes to these pairs to reduce data storage requirements, utilizing an analyzer, counting engine, and mapping engine to implement this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hard disk drives are used for database storage, then storage capacity is affordable, but disk access time is slow
Solution Approach 1:
The patent extracts frequently occurring attribute/value pairs from the database and stores them separately in a compression dictionary. This separation allows the main database to store only condensed codes, reducing storage needs and improving access speed for repetitive data patterns.
Solution Approach 2:
The patent creates a compression dictionary that serves as a reference copy for frequently occurring data patterns. Instead of storing redundant copies of repetitive attribute/value pairs throughout the database, the system stores a single master copy in the dictionary and references it via condensed codes, significantly reducing storage requirements.
2Speed
If solid state drives are used to improve disk access time, then data access speed improves, but storage cost increases
Solution Approach 1:
The patent changes the parameter of data representation by transforming full attribute/value pairs into condensed codes. This parameter change reduces the amount of data that needs to be stored and accessed, thereby reducing storage capacity requirements and associated costs while maintaining fast access performance.
3Quantity of substance
If data is stored without compression, then data retrieval is straightforward, but storage capacity requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-processing the database to identify and compress frequently occurring attribute/value pairs before actual data operations. The compression dictionary is built in advance, allowing subsequent read/write operations to use condensed codes without complex real-time processing, thus reducing both storage requirements and operational complexity.
Data Source
AI summary
A database compression system includes and analyzer, a counting engine, and a mapping engine. The analyzer analyzes a schema of a database by maintaining a list of attributes and corresponding values. The analyzer also analyzes a selection of entries in the database. The counting engine determines a frequency of occurrence of each attribute/value pair in the selection of entries. The mapping engine assigns a condensed code to a character string determined on the basis of the attribute/value pair with a highest frequency of occurrence.