Effectively Indexing Uncertain Moving Objects for Predictive Queries

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

Research output: Contribution to journalConference article in JournalResearchpeer-review

55 Citations (Scopus)

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.
Original languageEnglish
JournalInternational Conference on Very Large Data Bases. Proceedings
Issue number1
Pages (from-to)1198-1209
ISSN1047-7349
Publication statusPublished - 2009
EventInternational Conference on Very Large Database - Lyon, France
Duration: 24 Aug 200928 Aug 2009
Conference number: 35

Conference

ConferenceInternational Conference on Very Large Database
Number35
CountryFrance
CityLyon
Period24/08/200928/08/2009

Fingerprint

Location based services
Query processing
Experiments

Cite this

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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.",
author = "Meihui Zhang and Su Chen and Jensen, {Christian S{\o}ndergaard} and Ooi, {Beng Chin} and Zhenjie Zhang",
note = "Volumne: 2",
year = "2009",
language = "English",
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journal = "International Conference on Very Large Data Bases. Proceedings",
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Effectively Indexing Uncertain Moving Objects for Predictive Queries. / Zhang, Meihui; Chen, Su; Jensen, Christian Søndergaard; Ooi, Beng Chin; Zhang, Zhenjie.

In: International Conference on Very Large Data Bases. Proceedings, No. 1, 2009, p. 1198-1209.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

T1 - Effectively Indexing Uncertain Moving Objects for Predictive Queries

AU - Zhang, Meihui

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

SN - 1047-7349

IS - 1

ER -