Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

• Published in: Applied sciences Vol. 12; no. 21; p. 10942
• Main Authors: Rodríguez-Mazahua, Nidia, Rodríguez-Mazahua, Lisbeth, López-Chau, Asdrúbal, Alor-Hernández, Giner, Machorro-Cano, Isaac
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Algorithms; Big Data; cost model; data mining; data warehouse; Data warehouses; Decision making; decision tree; Decision trees; Fragmentation; horizontal fragmentation; Linear programming; Methods; Online analytical processing; Online transaction processing; Queries; Relational data bases; Response time (computers); Workloads
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122110942

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.