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

Abdeltawab M. Hendawi, Mohamed Ali, Mohamed Mokbel

Research output: Contribution to journalArticle

3 Citations (Scopus)

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
Pages (from-to)175-208
Number of pages34
JournalGeoInformatica
Volume21
Issue number2
DOIs
Publication statusPublished - 1 Apr 2017
Externally publishedYes

Fingerprint

Query processing
prediction
Shopping centers
traffic management
shopping center
Travel time
travel time
Scalability
Marketing
customer
travel
trajectory
Trajectories
employee
uncertainty
Personnel
traffic
resource
management
resources

Keywords

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

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

Panda : A generic and scalable framework for predictive spatio-temporal queries. / Hendawi, Abdeltawab M.; Ali, Mohamed; Mokbel, Mohamed.

In: GeoInformatica, Vol. 21, No. 2, 01.04.2017, p. 175-208.

Research output: Contribution to journalArticle

Hendawi, Abdeltawab M. ; Ali, Mohamed ; Mokbel, Mohamed. / Panda : A generic and scalable framework for predictive spatio-temporal queries. In: GeoInformatica. 2017 ; Vol. 21, No. 2. pp. 175-208.
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