Effectively Indexing Uncertain Moving Objects for Predictive Queries

Meihui Zhang, Su Chen, Christian Søndergaard Jensen, Beng Chin Ooi, Zhenjie Zhang

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

55 Citationer (Scopus)

Resumé

Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the Bx-tree that offer insights into the properties of the paper’s proposal.
OriginalsprogEngelsk
TidsskriftInternational Conference on Very Large Data Bases. Proceedings
Udgave nummer1
Sider (fra-til)1198-1209
ISSN1047-7349
StatusUdgivet - 2009
BegivenhedInternational Conference on Very Large Database - Lyon, Frankrig
Varighed: 24 aug. 200928 aug. 2009
Konferencens nummer: 35

Konference

KonferenceInternational Conference on Very Large Database
Nummer35
LandFrankrig
ByLyon
Periode24/08/200928/08/2009

Fingerprint

Location based services
Query processing
Experiments

Bibliografisk note

Volumne: 2

Citer dette

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title = "Effectively Indexing Uncertain Moving Objects for Predictive Queries",
abstract = "Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the Bx-tree that offer insights into the properties of the paper’s proposal.",
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Effectively Indexing Uncertain Moving Objects for Predictive Queries. / Zhang, Meihui; Chen, Su; Jensen, Christian Søndergaard; Ooi, Beng Chin; Zhang, Zhenjie.

I: International Conference on Very Large Data Bases. Proceedings, Nr. 1, 2009, s. 1198-1209.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

TY - GEN

T1 - Effectively Indexing Uncertain Moving Objects for Predictive Queries

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AU - Chen, Su

AU - Jensen, Christian Søndergaard

AU - Ooi, Beng Chin

AU - Zhang, Zhenjie

N1 - Volumne: 2

PY - 2009

Y1 - 2009

N2 - Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the Bx-tree that offer insights into the properties of the paper’s proposal.

AB - Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive experiments on the Bx-tree that offer insights into the properties of the paper’s proposal.

M3 - Conference article in Journal

SP - 1198

EP - 1209

JO - International Conference on Very Large Data Bases. Proceedings

JF - International Conference on Very Large Data Bases. Proceedings

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