Semantic Harmonizer: Matching Algorithm to Validate Data Transformations & Migrations

Abstract

Data transformation and migration processes demand robust mechanisms to ensure semantic consistency between datasets. This paper introduces a sophisticated algorithmic solution encapsulated in the Semantic Harmonizer [1] - a versatile library designed for matching data after transformation or migration. The Semantic Harmonizer employs a comprehensive approach, leveraging asynchronous processing, customizable resolution strategies, and a set of predefined semantic matching issues to address differences systematically [1]. Key components of this library include interfaces for basic semantic units, generators for unit production, and utility functions for handling complex data structures. The library contributes a vital tool to enhance the reliability and accuracy of semantic matching in diverse scenarios involving data transformation or migration. The proposed algorithm offers flexibility, efficiency, and a systematic approach to resolving semantic differences, making it an invaluable asset in the realm of data management and system evolution [2]. This paper delves into the overall architecture, explicates design choices, and imparts insights into best practices for implementing and maintaining such a library, leveraging contemporary tools. The discussion encompasses critical aspects of the library’s functionality, emphasizing the need for scalability, flexibility, and resilience to meet the demands of modern data - driven applications [3][4].