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Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences

Mumolo, Enzo
•
Moussa, Rim
•
Cuzzocrea, Alfredo
2018
  • conference object

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.
DOI
10.5220/0006812202930298
Archivio
http://hdl.handle.net/11368/2928910
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85047763218
Diritti
closed access
license:copyright editore
FVG url
https://arts.units.it/request-item?handle=11368/2928910
Soggetti
  • DATA WAREHOUSE TUNING...

  • OLAP INTELLIGENCE

  • DATA WAREHOUSE WORKLO...

  • OLAP WORKLOADS

Visualizzazioni
9
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
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