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Predictive Nearest Neighbor Queries over Uncertain Spatial-Temporal Data

  • Jinghua Zhu
  • Xue Wang
  • Yingshu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8491)

Abstract

Predictive nearest neighbor queries over spatial-temporal data have received significant attention in many location-based services including intelligent transportation, ride sharing and advertising. Due to physical and resource limitations of data collection devices like RFID, sensors and GPS, data is collected only at discrete time instants. In-between these discrete time instants, the positions of the monitored moving objects are uncertain. In this paper, we exploit the filtering and refining framework to solve the predictive nearest neighbor queries over uncertain spatial-temporal data. Specifically, in the filter phase, our approach employs a semi-Markov process model that describes object mobility between space grids and prunes those objects that have zero probability to encounter the queried object. In the refining phase, we use a Markov chain model to describe the mobility of moving objects between space points and compute the nearest neighbor probability for each candidate object. We experimentally show that our approach can filter out most of the impossible objects and has a good predication performance.

Keywords

spatial-temporal data uncertain data predictive query Markov chain semi-Markov model 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jinghua Zhu
    • 1
  • Xue Wang
    • 1
  • Yingshu Li
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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