Attraction and avoidance detection from movements

Zhenhui Li, Bolin Ding, Fei Wu, Tobias Kin Hou Lei, Roland Kays, Margaret C. Crofoot

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

With the development of positioning technology, movement data has become widely available nowadays. An important task in movement data analysis is to mine the relationships among moving objects based on their spatiotemporal interactions. Among all relationship types, attraction and avoidance are arguably the most natural ones. However, rather surprisingly, there is no existing method that addresses the problem of mining significant attraction and avoidance relationships in a well-defined and unified framework. In this paper, we propose a novel method to measure the significance value of relationship between any two objects by examining the background model of their movements via permutation test. Since permutation test is computationally expensive, two effective pruning strategies are developed to reduce the computation time. Furthermore, we show how the proposed method can be extended to effeciently answer the classic threshold query: given an object, retrieve all the objects in the database that have relationships, whose significance values are above certain threshold, with the query object. Empirical studies on both synthetic data and real movement data demonstrate the effectiveness and effeciency of our method.

Original languageEnglish (US)
Pages (from-to)157-168
Number of pages12
JournalProceedings of the VLDB Endowment
Volume7
Issue number3
DOIs
StatePublished - Nov 2013

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Li, Z., Ding, B., Wu, F., Lei, T. K. H., Kays, R., & Crofoot, M. C. (2013). Attraction and avoidance detection from movements. Proceedings of the VLDB Endowment, 7(3), 157-168. https://doi.org/10.14778/2732232.2732235
Li, Zhenhui ; Ding, Bolin ; Wu, Fei ; Lei, Tobias Kin Hou ; Kays, Roland ; Crofoot, Margaret C. / Attraction and avoidance detection from movements. In: Proceedings of the VLDB Endowment. 2013 ; Vol. 7, No. 3. pp. 157-168.
@article{123ff341724d4ab1a4d08f6c6aabd35f,
title = "Attraction and avoidance detection from movements",
abstract = "With the development of positioning technology, movement data has become widely available nowadays. An important task in movement data analysis is to mine the relationships among moving objects based on their spatiotemporal interactions. Among all relationship types, attraction and avoidance are arguably the most natural ones. However, rather surprisingly, there is no existing method that addresses the problem of mining significant attraction and avoidance relationships in a well-defined and unified framework. In this paper, we propose a novel method to measure the significance value of relationship between any two objects by examining the background model of their movements via permutation test. Since permutation test is computationally expensive, two effective pruning strategies are developed to reduce the computation time. Furthermore, we show how the proposed method can be extended to effeciently answer the classic threshold query: given an object, retrieve all the objects in the database that have relationships, whose significance values are above certain threshold, with the query object. Empirical studies on both synthetic data and real movement data demonstrate the effectiveness and effeciency of our method.",
author = "Zhenhui Li and Bolin Ding and Fei Wu and Lei, {Tobias Kin Hou} and Roland Kays and Crofoot, {Margaret C.}",
year = "2013",
month = "11",
doi = "10.14778/2732232.2732235",
language = "English (US)",
volume = "7",
pages = "157--168",
journal = "Proceedings of the VLDB Endowment",
issn = "2150-8097",
publisher = "Very Large Data Base Endowment Inc.",
number = "3",

}

Li, Z, Ding, B, Wu, F, Lei, TKH, Kays, R & Crofoot, MC 2013, 'Attraction and avoidance detection from movements', Proceedings of the VLDB Endowment, vol. 7, no. 3, pp. 157-168. https://doi.org/10.14778/2732232.2732235

Attraction and avoidance detection from movements. / Li, Zhenhui; Ding, Bolin; Wu, Fei; Lei, Tobias Kin Hou; Kays, Roland; Crofoot, Margaret C.

In: Proceedings of the VLDB Endowment, Vol. 7, No. 3, 11.2013, p. 157-168.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Attraction and avoidance detection from movements

AU - Li, Zhenhui

AU - Ding, Bolin

AU - Wu, Fei

AU - Lei, Tobias Kin Hou

AU - Kays, Roland

AU - Crofoot, Margaret C.

PY - 2013/11

Y1 - 2013/11

N2 - With the development of positioning technology, movement data has become widely available nowadays. An important task in movement data analysis is to mine the relationships among moving objects based on their spatiotemporal interactions. Among all relationship types, attraction and avoidance are arguably the most natural ones. However, rather surprisingly, there is no existing method that addresses the problem of mining significant attraction and avoidance relationships in a well-defined and unified framework. In this paper, we propose a novel method to measure the significance value of relationship between any two objects by examining the background model of their movements via permutation test. Since permutation test is computationally expensive, two effective pruning strategies are developed to reduce the computation time. Furthermore, we show how the proposed method can be extended to effeciently answer the classic threshold query: given an object, retrieve all the objects in the database that have relationships, whose significance values are above certain threshold, with the query object. Empirical studies on both synthetic data and real movement data demonstrate the effectiveness and effeciency of our method.

AB - With the development of positioning technology, movement data has become widely available nowadays. An important task in movement data analysis is to mine the relationships among moving objects based on their spatiotemporal interactions. Among all relationship types, attraction and avoidance are arguably the most natural ones. However, rather surprisingly, there is no existing method that addresses the problem of mining significant attraction and avoidance relationships in a well-defined and unified framework. In this paper, we propose a novel method to measure the significance value of relationship between any two objects by examining the background model of their movements via permutation test. Since permutation test is computationally expensive, two effective pruning strategies are developed to reduce the computation time. Furthermore, we show how the proposed method can be extended to effeciently answer the classic threshold query: given an object, retrieve all the objects in the database that have relationships, whose significance values are above certain threshold, with the query object. Empirical studies on both synthetic data and real movement data demonstrate the effectiveness and effeciency of our method.

UR - http://www.scopus.com/inward/record.url?scp=84887506082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887506082&partnerID=8YFLogxK

U2 - 10.14778/2732232.2732235

DO - 10.14778/2732232.2732235

M3 - Article

AN - SCOPUS:84887506082

VL - 7

SP - 157

EP - 168

JO - Proceedings of the VLDB Endowment

JF - Proceedings of the VLDB Endowment

SN - 2150-8097

IS - 3

ER -