Efficient OLAP operations in spatial data warehouses

Dimitris Papadias, Panos Kalnis, Jun Zhang, Yufei Tao

Research output: Contribution to journalArticlepeer-review

180 Scopus citations

Abstract

Spatial databases store information about the position of individual objects in space. In many applications however, such as traffic supervision or mobile communications, only summarized data, like the number of cars in an area or phones serviced by a cell, is required. Although this information can be obtained from transactional spatial databases, its computation is expensive, rendering online processing inapplicable. Driven by the non-spatial paradigm, spatial data warehouses can be constructed to accelerate spatial OLAP operations. In this paper we consider the star-schema and we focus on the spatial dimensions. Unlike the non-spatial case, the groupings and the hierarchies can be numerous and unknown at design time, therefore the well-known materialization techniques are not directly applicable. In order to address this problem, we construct an ad-hoc grouping hierarchy based on the spatial index at the finest spatial granularity. We incorporate this hierarchy in the lattice model and present efficient methods to process arbitrary aggregations. We finally extend our technique to moving objects by employing incremental update methods.

Original languageEnglish (US)
Pages (from-to)443-459
Number of pages17
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2121
DOIs
StatePublished - Jan 1 2001

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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