Database Object Segmentation for M&A Data Transfer
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Solution Overview
Problem
In mergers and acquisitions, companies face challenges in selectively transferring and managing large amounts of data while maintaining business continuity, as existing technologies lack efficient methods for precise data selection, separation, and transmission based on specific requirements.
Innovation Solution
A method and system that utilize a computer device to group database data into objects based on table dependencies, classify them by application area, data type, and characteristics, and set selection criteria for target data extraction, allowing for data modulation through selection, combination, exclusion, or addition for purposes like archiving, cleansing, transmission, conversion, or encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is selectively transferred during M&A, then business continuity is maintained and data transmission efficiency is improved, but data selection precision and completeness are compromised
Solution Approach 1:
The patent segments data into objects based on table dependencies, where each object represents a minimum process processing unit. This segmentation allows selective extraction of specific data objects while maintaining internal consistency within each object, thereby improving transmission efficiency without sacrificing selection precision. The segmentation is achieved by identifying dependency relationships between tables and grouping them into logical units.
Solution Approach 2:
The patent performs preliminary classification of data objects based on application areas, data types, and characteristics before actual data extraction. This preliminary action enables the system to pre-identify which data objects should be transferred, allowing for precise and efficient selective data transmission during M&A processes without compromising completeness.
2Reliability
If all data is transferred during M&A, then data completeness is ensured, but data management complexity and processing time increase
Solution Approach 1:
By segmenting data into dependency-based objects, the patent reduces data management complexity while ensuring completeness within each object. Instead of managing all data as a single entity, the system manages discrete objects that can be independently processed, classified, and transferred, thereby reducing overall complexity while maintaining reliability.
Solution Approach 2:
The patent applies partial action by selectively transferring only the necessary data objects based on classification criteria (application area, data type, characteristics) rather than transferring all data. This approach ensures data completeness for the required scope while significantly reducing processing time and management complexity compared to transferring entire datasets.
3Measurement precision
If data is classified by multiple criteria, then data selection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the classification process into distinct dimensions: application area, data type, and characteristics. Each dimension is processed independently to classify data objects, which improves selection accuracy through multi-criteria evaluation while managing complexity by treating each criterion as a separate classification stage rather than a complex integrated process.
Data Source
AI summary
Disclosed are a method and system for selecting target data. The method of selecting target data may include steps of grouping data included in a database into objects each being a minimum process processing unit based on dependency between tables within the database, setting a selection criterion for target data based on at least one field of the data included in the database, and extracting target data corresponding to the set selection criterion by taking into consideration dependency between tables included in the object.