Indexing the Past, Present and Anticipated Future Positions of Moving Objects

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Abstract

With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This paper offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially extends partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.
Original languageEnglish
JournalACM Transactions on Database Systems
Volume31
Issue number1
Pages (from-to)255-298
Number of pages44
ISSN0362-5915
Publication statusPublished - 2006

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title = "Indexing the Past, Present and Anticipated Future Positions of Moving Objects",
abstract = "With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This paper offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially extends partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.",
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Indexing the Past, Present and Anticipated Future Positions of Moving Objects. / Pelanis, M.; Saltenis, Simonas; Jensen, Christian Søndergaard.

In: ACM Transactions on Database Systems, Vol. 31, No. 1, 2006, p. 255-298.

Research output: Contribution to journalJournal articleResearchpeer-review

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AB - With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This paper offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially extends partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.

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