A hybrid prediction model for moving objects

Jeung, Hoyoung, Liu, Qing, Shen, Heng Tao and Zhou, Xiaofang (2008). A hybrid prediction model for moving objects. In: , IEEE 24th International Conference on Data Engineering, 2008(ICDE 2008). IEEE 24th International Conference on Data Engineering, 2008 (ICDE 2008), Cancun, Mexico, (70-79). 7 -12 April 2008.

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Author Jeung, Hoyoung
Liu, Qing
Shen, Heng Tao
Zhou, Xiaofang
Title of paper A hybrid prediction model for moving objects
Conference Paper Type Fully Published Paper
Conference name IEEE 24th International Conference on Data Engineering, 2008 (ICDE 2008)    (ERA 2010 Rank A)
DOI 10.1109/ICDE.2008.4497415
Conference location Cancun, Mexico
Conference dates 7 -12 April 2008
Proceedings title IEEE 24th International Conference on Data Engineering, 2008(ICDE 2008)   Check publisher's open access policy
Journal name 2008 Ieee 24th International Conference On Data Engineering, Vols 1-3   Check publisher's open access policy
Place published Piscataway, NJ, United States
Publisher IEEE
Publication date 2008
ISBN 9781424418367; 9781424418374
ISSN 1084-4627
Start page 70
End page 79
Total pages 10
Collection year 2009
Language eng
Abstract/Summary Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes.
Subjects E1
890205 Information Processing Services (incl. Data Entry and Capture)
080604 Database Management
Keyword Query processing
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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