Efficient aggregation over objects with extent

  • Authors:

  • Donghui Zhang;Vassilis J. Tsotras;Dimitrios Gunopulos

  • Affiliations:

  • University of California, Riverside, CA;University of California, Riverside, CA;University of California, Riverside, CA

  • Venue:
  • Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
  • Year:
  • 2002

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Abstract

We examine the problem of efficiently computing sum/count/avg aggregates over objects with non-zero extent. Recent work on computing multi-dimensional aggregates has concentrated on objects with zero extent (points) on a multi-dimensional grid, or one-dimensional intervals. However, in many spatial and/or spatio-temporal applications objects have extent in various dimensions, while they can be located anywhere in the application space. The aggregation predicate is typically described by a multi-dimensional box (box-sum aggregation). We examine two variations of the problem. In the simple case an object's value contributes to the aggregation result as a whole as long as the object intersects the query box. More complex is the functional box-sum aggregation introduced in this paper, where objects participate in the aggregation proportionally to the size of their intersection with the query box. We first show that both problems can he reduced to dominance-sum queries. Traditionally dominance-sum queries are addressed in main memory by a static structure, the ECDF-tree. We then propose two extensions, namely the ECDF-B-trees, that make this structure disk-based and dynamic. Finally, we introduce the DA-tree that combines the advantages from each ECDF-B-tree. We run experiments comparing the performance of the ECDF-B-trees, the BA-tree and a traditional R*-tree (which has been augmented to include aggregation information on its index nodes) over spatial datasets. Our evaluation reaffirms that the BA-tree has more robust performance. Compared against the augmented R*-tree, the BA-tree offers drastic improvement in query performance at the expense of some limited extra space.