Indexing Vague Regions in Spatial Data Warehouses Thiago Luís Lopes Siqueira 1,2 , João Celso Santos de Oliveira 1 , Valéria Cesário Times 3 Cristina Dutra de Aguiar Ciferri 4 , Ricardo Rodrigues Ciferri 1 1 Department of Computer Science, Federal University of São Carlos, UFSCar, 13.565-905, São Carlos, SP, Brazil 2 São Paulo Federal Institute of Education, Science and Technology, IFSP, 13.565-905, São Carlos, SP, Brazil. 3 Informatics Center, Federal University of Pernambuco, UFPE, 50.670-901, Recife, PE, Brazil 4 Department of Computer Science, University of São Paulo, USP, 13.560-970, São Carlos, SP, Brazil prof.thiago@ifsp.edu.br, joaocelso@comp.ufscar.br, vct@cin.ufpe.br cdac@icmc.usp.br, ricardo@dc.ufscar.br Abstract. A vague spatial data warehouse allows multidimensional queries with spatial predicates to support the analysis of business scores related to vague spatial data, addressing real world phenomena characterized by inexact locations or indeterminate boundaries. However, vague spatial data are usually represented and stored as multiple geometries and impair the query processing performance. In this paper, we introduce an index called VSB-index to improve the query processing performance in vague spatial data warehouses, focusing on range queries and vague regions. We also conduct an experimental evaluation demonstrating that our VSB-index provided remarkable performance gains up to 97% over existing solutions. 1. Introduction Decision-making support has gained the attention of researchers of Geographic Information System (GIS), Data Warehouse (DW) and Online Analytical Processing (OLAP). Fast, flexible, and multidimensional ways for spatial data analysis are provided by Spatial OLAP tools that query a Spatial Data Warehouse (SDW), which is a subject- oriented, integrated, time-variant, voluminous, non-volatile and multidimensional database that mainly stores crisp spatial data as vector (e.g. political boundaries) and their descriptive attributes (conventional data) [Bimonte et al. 2010]. A fact denotes the scores of business activities through numeric measures or spatial measures, while dimensions hold conventional attributes and spatial attributes that contextualize values of measures. Usually, spatial range queries concerning ad hoc spatial query windows select specific spatial objects stored in the SDW, e.g. intersection range query (IRQ) [Siqueira et al. 2012a]. The performance of query processing is a critical issue in SDW and motivates the design of indices to reduce the elapsed time to join huge tables, process spatial predicates and aggregate voluminous data [Papadias et al. 2001; Siqueira et al. 2012a]. Mainly, SDWs store crisp spatial data. On the other hand, several real world phenomena are affected by spatial vagueness, which is one kind of spatial data Proceedings of XIV GEOINFO, November 24-27, 2013, Campos do Jord˜ ao, Brazil. 158