Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences

Alfredo Cuzzocrea, Rim Moussa, Enzo Mumolo

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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