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GeoInformatica

, Volume 21, Issue 2, pp 175–208 | Cite as

Panda : A generic and scalable framework for predictive spatio-temporal queries

  • Abdeltawab M. Hendawi
  • Mohamed AliEmail author
  • Mohamed F. Mokbel
Article

Abstract

Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects’ historical trajectories. (3) Panda is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda in evaluating a massive volume of predictive queries over large numbers of moving objects.

Keywords

Predictive Spatiotemporal Query processing KNN queries Range queries Panda 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Abdeltawab M. Hendawi
    • 1
  • Mohamed Ali
    • 2
    Email author
  • Mohamed F. Mokbel
    • 3
  1. 1.Department of Computer ScienceUniversity of Virginia - CharlottesvilleCharlottesvilleUSA
  2. 2.Center for Data Science, Institute of TechnologyUniversity of Washington TacomaTacomaUSA
  3. 3.Department of Computer Science and EngineeringUniversity of Minnesota - Twin CitiesMinneapolisUSA

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