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EnglishIn 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.
| Titolo: | Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences | |
| Autori: | ||
| Data di pubblicazione: | 2018 | |
| 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. | |
| Handle: | http://hdl.handle.net/11368/2928910 | |
| ISBN: | 978-989-758-298-1 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
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