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

Abdeltawab M. Hendawi, Mohamed Ali, Mohamed F. Mokbel

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)175-208
Number of pages34
JournalGeoInformatica
Volume21
Issue number2
DOIs
StatePublished - Apr 1 2017

Bibliographical note

Funding Information:
The research of these authors is supported in part by the National Science Foundation under Grants IIS-0952977 and IIS-1218168

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • KNN queries
  • Panda
  • Predictive
  • Query processing
  • Range queries
  • Spatiotemporal

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