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Authors: Alfredo Cuzzocrea 1 ; Rim Moussa 2 and Enzo Mumolo 3

Affiliations: 1 University of Trieste and ICAR-CNR, Italy ; 2 LaTICE and University of Carthage, Tunisia ; 3 University of Trieste, Italy

ISBN: 978-989-758-298-1

Keyword(s): Data Warehouse Tuning, OLAP Intelligence, Data Warehouse Workloads, OLAP Workloads.

Abstract: In order to tune a data warehouse workload, we need automated recommenders on when and how (i) to partition data and (ii) to deploy summary structures such as derived attributes, aggregate tables, and (iii) to build OLAP indexes. In this paper, we share our experience of implementation of an OLAP workload analyzer, which exhaustively enumerates all materialized views, indexes and fragmentation schemas candidates. As a case of study, we consider TPC-DS benchmark -the de-facto industry standard benchmark for measuring the performance of decision support solutions including.

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Paper citation in several formats:
Cuzzocrea, A.; Moussa, R. and Mumolo, E. (2018). Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 293-298. DOI: 10.5220/0006812202930298

@conference{iceis18,
author={Alfredo Cuzzocrea. and Rim Moussa. and Enzo Mumolo.},
title={Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={293-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006812202930298},
isbn={978-989-758-298-1},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences
SN - 978-989-758-298-1
AU - Cuzzocrea, A.
AU - Moussa, R.
AU - Mumolo, E.
PY - 2018
SP - 293
EP - 298
DO - 10.5220/0006812202930298

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