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On moving object queries: (extended abstract)
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Source Symposium on Principles of Database Systems archive
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents
Madison, Wisconsin
SESSION: Research session 6: OLAP and constraints table of contents
Pages: 188 - 198  
Year of Publication: 2002
ISBN:1-58113-507-6
Authors
Hoda Mokhtar  University of California, Santa Barbara, CA
Jianwen Su  University of California, Santa Barbara, CA
Oscar Ibarra  University of California, Santa Barbara, CA
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 45,   Citation Count: 9
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ABSTRACT

Database applications for moving objects pose new challenges in modeling, querying, and maintenance of objects whose locations are rapidly changing over time. Previous work on modeling and querying spatio-temporal databases and constraint databases focus primarily on snapshots of changing databases. In this paper we study query evaluation techniques for moving object databases where moving objects are being updated frequently. We consider a constraint database approach to moving objects and queries. We classify moving object queries into: "past", "continuing", and "future" queries. We argue that while traditional constraint query evaluation techniques are suitable for past queries, new techniques are needed for continuing and future queries. Motivated by nearest-neighbor queries, we define a query language based on a single "generalized distance" function f mapping from objects to continuous functions from time to ℝ. Queries in this language may be past, continuing, or future. We show that if f maps to polynomials, queries can be evaluated efficiently using the plane sweeping technique from computational geometry. Consequently, many known distance based queries can be evaluated efficiently.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  9

Collaborative Colleagues:
Hoda Mokhtar: colleagues
Jianwen Su: colleagues
Oscar Ibarra: colleagues