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
Keyword(s):
Data Warehouse Tuning, OLAP Intelligence, Data Warehouse Workloads, OLAP Workloads.
Related
Ontology
Subjects/Areas/Topics:
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Performance Evaluation and Benchmarking
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.