Taxonomy Evolution Data Structure Generation
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Solution Overview
Problem
Existing data processing systems face challenges in reliably and efficiently updating taxonomies over time, leading to errors and inefficiencies when handling evolving data structures and terms.
Innovation Solution
A method and system for generating a data structure that illustrates the evolution of taxonomies by analyzing historic and new taxonomy data, creating associations, and generating metadata to understand changes, allowing downstream consumers to interpret these changes effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If taxonomy data is updated over time to reflect changing business requirements, then adaptability improves, but data consistency and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by creating a mapping between old and new taxonomy versions before actual data updates occur. This mapping serves as a translation layer that preserves data consistency while allowing taxonomy evolution, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The invention introduces an intermediary mapping structure that mediates between the old taxonomy version and the new taxonomy version. This intermediary layer allows seamless translation of references, enabling taxonomy updates without compromising data consistency or reliability.
2Reliability
If manual updates are performed on data structures when taxonomies change, then data accuracy can be maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system implements self-service by automatically generating updated data structures through the mapping mechanism. Instead of requiring manual intervention, the system autonomously translates references from old to new taxonomy versions, maintaining data accuracy while dramatically improving productivity.
Solution Approach 2:
The invention replaces the mechanical manual update process with an automated computational system. The mapping-based translation mechanism substitutes human effort with algorithmic processing, preserving data accuracy while eliminating the productivity bottleneck of manual updates.
3Loss of information
If comprehensive analysis of taxonomy changes is performed, then understanding and interpretation improve, but complexity of the process deteriorates
Solution Approach 1:
The system segments the complex taxonomy evolution analysis into manageable components: creating the mapping, storing it in the database, and applying it during updates. This segmentation reduces processing complexity while maintaining comprehensive change analysis and understanding.
Solution Approach 2:
By performing the complex analysis work in advance to create the mapping, the system separates the complexity of understanding taxonomy changes from the simplicity of applying the mapping. The preliminary analysis phase captures all complexity, while subsequent updates benefit from the simplified mapping structure.
Data Source
AI summary
Various methods, apparatuses/systems, and media for generating a data structure are provided. A database stores a historic version of taxonomy data and a new version of taxonomy data. A processor, operatively connected to the database, accesses the database and analyzes the historic version of taxonomy data and the new version of taxonomy data. The processor determines what changes have been made in connection with a particular reference data based on analyzing the historic version of taxonomy data and the new version of taxonomy data; creates, based on determining, an association between the historic version of taxonomy data and the new version of taxonomy data corresponding to said particular reference data; generates consistent metadata from said association; and generates a data structure that illustrates history of evolution of taxonomy in connection with said particular reference data based on the metadata.