Open AccessProRes: Proactive re-selection of materialized views
Author(s) -
Mustapha Chaba Mouna,
Ladjel Bellatreche,
Narhimène Boustia
Publication year - 2022
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis210606003m
Subject(s) - materialized view , computer science , selection (genetic algorithm) , sql , set (abstract data type) , field (mathematics) , nosql , data warehouse , task (project management) , hypergraph , database , data mining , information retrieval , view , big data , artificial intelligence , database design , mathematics , management , discrete mathematics , pure mathematics , economics , programming language
Materialized View Selection is one of the most studied problems in the database field, covering SQL and NoSQL technologies as well as different deployment infrastructures (centralized, parallel, cloud). This problem has become more complex with the arrival of data warehouses, being coupled with the physical de sign phase that aims at optimizing query performance. Selecting the best set of materialized views to optimize query performance is a challenging task. Given their importance and the complexity of their selection, several research efforts both from academia and industry have been conducted. Results are promising - some solutions are being implemented by commercial and open-source DBMSs -, but they do not factor in the following properties of nowadays analytical queries: (i) large scale queries, (ii) their dynamicity, and (iii) their high interaction. Studies to date fail to consider that complete set of properties. Considering the three properties simultaneously is crucial regarding today?s analytical requirements, which involve dynamic and interactive queries. In this paper, we first present a concise state of the art of the materialized view selection problem (VSP) by analyzing its ecosystem. Secondly, we propose a proactive re-selection approach that considers the three properties concurrently. It features twomain phases: offline and online. In the offline phase, we manage a set of the first queries based on a given threshold _ by selecting materialized views through a hypergraph structure. The second phase manages the addition of new queries by scheduling them, updates the structure of the hypergraph, and selects new views by eliminating the least beneficial ones. Finally, extensive experiments are conducted using the Star Schema Benchmark data set to evaluate the effectiveness and efficiency of our approach.