Database Data Comparison Using Frequency-Based Tree Structures
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Solution Overview
Problem
Current database comparison methods are inefficient and resource-intensive, especially for large databases, as they often require row-by-row or column-by-column comparisons, which lead to high time consumption and resource usage, and are not effective in identifying differences accurately.
Innovation Solution
The implementation of a tree structure-based comparison method, where nodes indicate characteristic values of data segments and are divided into branches based on update frequencies, allowing for efficient comparison by analyzing the tree structures instead of the raw data, thereby improving efficiency and accuracy in localizing differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If row-by-row or column-by-column comparison methods are used for database comparison, then comprehensive data comparison can be achieved, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent divides the database comparison task into segments by creating tree structures where nodes represent data segments (rows, columns, or groups of cells). This segmentation allows the comparison to proceed hierarchically from root nodes to leaf nodes, comparing only relevant segments rather than all data, thereby reducing time consumption while maintaining comprehensive comparison accuracy.
Solution Approach 2:
The patent transforms the traditional flat database comparison into a hierarchical dimensional structure using trees. By organizing data segments into multiple levels (root nodes representing entire tables, intermediate nodes representing subsets, and leaf nodes representing individual cells), the system adds a dimensional aspect to comparison, enabling efficient traversal and localization of differences without exhaustive row-by-row scanning.
2Reliability
If traditional database comparison methods are used, then all data segments can be examined, but resource consumption increases
Solution Approach 1:
The patent performs preliminary actions by constructing tree structures that pre-organize data segments into a hierarchical format with computed characteristic values (such as hash values or checksums) at each node. This preliminary organization enables rapid comparison by allowing the system to skip entire subtrees when characteristic values match, significantly reducing computational resource usage while maintaining reliable detection of all actual differences.
Solution Approach 2:
The patent introduces tree structures as intermediary representations between the raw database data and the comparison process. These trees act as mediators that summarize data segments at each level, allowing the comparison system to work with condensed representations rather than raw data, thereby reducing computational overhead while preserving the ability to detect differences reliably.
3Productivity
If unbalanced tree structures with branches based on update frequencies are used, then traversal time is shortened, but device complexity increases
Solution Approach 1:
The patent applies local quality by creating unbalanced tree structures where branches are differentiated based on local characteristics of the data, specifically update frequencies. Frequently updated data segments are placed in branches that are traversed or validated more rigorously, while less frequently updated segments use simpler validation. This localized adaptation optimizes comparison efficiency for each data segment's actual behavior patterns.
Solution Approach 2:
The patent changes structural parameters of the tree based on data characteristics, specifically using update frequency as a parameter to determine tree topology. By dynamically adjusting the tree structure (creating unbalanced trees with varying branch depths and configurations) based on how frequently different data segments are updated, the system optimizes comparison efficiency without requiring a completely complex system, as the complexity adapts to the actual data usage patterns.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, system, and computer program product for comparison of database data. According to the method, a first tree structure corresponding to first data segments of first database data and a second tree structure corresponding to second data segments of second database data are at least partially obtained. Each node of the first or second tree structure indicating a characteristic value of at least one of the first or second data segments, and nodes of the first or second tree structure are divided into a first or second plurality of branches from a first or second root node based on update frequencies of the first or second data segments. A difference between the first data segments and the second data segments is determined by at least comparing characteristic values indicated by nodes in the obtained parts of the first and second tree structures.