A SURVEY ON CHANGE DETECTION IN HIERARCHICAL DATA STRUCTURES FOR DATA WAREHOUSES

Change detection algorithms play a crucial role in data warehousing applications, where efficiently identifying and tracking differences between data structures is essential. This paper presents a comprehensive review of existing change detection algorithms, focusing on their methodologies, assumptions, and performance characteristics in the context of data warehouse environments. The review emphasizes the applicability and results of these algorithms considering factors such as matching accuracy, handling of moved subtrees, support for subtree deletion, and overall effectiveness in capturing changes. Experimental evaluations and comparative analyses are conducted to assess the performance of the algorithms. Some other applications such as web publishing, query systems, and database management, version and configuration management are also discussed. The findings of this review provide valuable insights into the state-of-the-art change detection algorithms, enabling researchers and practitioners in the field of data warehousing to make informed decisions when selecting and implementing change detection solutions.