Benchmarking spatial data warehouses

Authors:
Thiago Luís Lopes Siqueira;Ricardo Rodrigues Ciferri;Valéria Cesário Times;Cristina Dutra De Aguiar Ciferri
Affiliations:
São Paulo Federal Institute of Education, Science and Technology, Salto, SP, Brazil and Computer Science Department, Federal University of São Carlos, São Carlos, SP, Brazil;Computer Science Department, Federal University of São Carlos, São Carlos, SP, Brazil;Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil;Computer Science Department, University of São Paulo at São Carlos, São Carlos, SP, Brazil
Venue:
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Year:
2010

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Abstract

Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different spatial granularity levels. Several techniques to improve the query processing performance over SDW have been proposed in the literature. However, we identified the lack of a benchmark to carry out a controlled experimental evaluation of such techniques and, principally, to effectively measure the costs of the aforementioned three complex operations. In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to provide performance evaluation environments for SDW and enable a further investigation on spatial data redundancy. The Spadawan benchmark is available at http://gbd.dc.ufscar.br/spadawan.