Indexing spatio-temporal data warehouses

Dimitris Papadias, Yufei Tao, Panos Kalnis, Jun Zhang

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

Spatio-temporal databases store information about the positions of individual objects over time. In many applications however, such as traffic supervision or mobile communication systems, only summarized data, like the average number of cars in an area for a specific period, or phones serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation is expensive, rendering online processing inapplicable. A vital solution is the construction of a spatiotemporal data warehouse. In this paper, we describe framework for supporting OLAP operations over spatiotemporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and present data structures, which integrate spatiotemporal indexing with pre-aggregation. While the well-known materialization techniques require a-priori knowledge of the grouping hierarchy, we develop methods that utilize the proposed structures for efficient execution of ad-hoc group-bys. Our techniques can be used for both static and dynamic dimensions.

Original languageEnglish (US)
Pages (from-to)166-175
Number of pages10
JournalProceedings - International Conference on Data Engineering
DOIs
StatePublished - Jan 1 2002

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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