Title: Efficient of bitmap join indexes for optimising star join queries in relational data warehouses

Authors: Mohammed Yahyaoui; Souad Amjad; Lamia Benameur; Ismail Jellouli

Addresses: Information Technology and Modeling Systems Research Unit (TIMS), Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco ' Information Technology and Modeling Systems Research Unit (TIMS), Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco ' Information Technology and Modeling Systems Research Unit (TIMS), Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco ' Information Technology and Modeling Systems Research Unit (TIMS), Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco

Abstract: Data warehouses are dedicated to analysis and decision-making applications. They are often schematised as star relational models or variants for on-line analysis. Typically, the analysis process is conducted via online analytical processing (OLAP) type queries. These queries are usually complex, characterised by multiple selections operations, joins, grouping and aggregations on large tables. Which require a lot of calculation time and thus a very high response time. The performance of these queries depends directly on the use of the secondary memory. Indeed, each input-output on disk requiring up to ten milliseconds. In order to reduce and minimise the cost of executing these queries, the data warehouse administrator must make a good physical design during the physical design and tuning phase by optimising access to the secondary memory. We focus on bitmap join indexes that share the same resource, that is, the selection attributes extracted from the business intelligence queries. To optimise star join queries.

Keywords: data warehouse; OLAP; indexes; optimisation query; star join query; bitmap join indexes.

DOI: 10.1504/IJCISTUDIES.2020.109604

International Journal of Computational Intelligence Studies, 2020 Vol.9 No.3, pp.220 - 233

Received: 11 Jan 2019
Accepted: 02 Aug 2019

Published online: 04 Sep 2020 *

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