Hierarchical Data Space Subdivision for Efficient Tuple Access
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Solution Overview
Problem
Existing relational data management systems face inefficiencies in data operations due to their reliance on row or columnar approaches, which require scanning entire columns of values for operations, leading to performance issues.
Innovation Solution
The implementation of a data management system that utilizes hierarchical data spaces, allowing for the determination of subdivisions within logical hierarchical data spaces, and the reading of tuples from corresponding files, thereby optimizing data operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relational data management systems use row or columnar approaches to organize data, then data can be stored and retrieved by comparing or sorting values, but the system must scan the entire column of values to perform operations, leading to performance issues
Solution Approach 1:
The patent segments the data space into hierarchical subdivisions, where each subdivision contains a subset of data tuples. This allows the system to operate on specific subdivisions rather than scanning entire columns, dramatically reducing the data volume that must be processed for each operation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional row/column data organization. By adding hierarchical levels above the traditional data structure, the system can navigate and operate on data subsets through hierarchical path identifiers, transforming the operational approach from linear scanning to hierarchical navigation.
2Measurement precision
If the system performs data operations by value for relevant attributes, then accurate results are obtained, but the complexity of value-based comparisons increases with data size
Solution Approach 1:
The patent introduces hierarchical path identifiers as intermediaries between the data storage structure and the query operation. These identifiers enable the system to locate and operate on specific data subsets without performing comprehensive value-based comparisons across the entire dataset, reducing computational complexity while maintaining operational accuracy.
Data Source
AI summary
Methods and apparatus for executing a data operation are described herein. The methods and systems may include determining at least one subdivision of at least one logical hierarchical data space. The at least one logical hierarchical data space may have a plurality of subdivisions. The method may further include determining at least one file corresponding to the at least one subdivision of the at least one logical hierarchical data space. The method may further include reading at least one tuple from the at least one file.