Panda: A predictive spatio-temporal query processor

Abdeltawab M. Hendawi, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

This paper presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries that are widely used in several applications including traffic management, location-based advertising, and ride sharing. Unlike previous attempts in supporting predictive queries, Panda targets long-term query prediction as it relies on adapting a well-designed long-term prediction function to: (a) scale up to large number of moving objects, and (b) support large number of predictive queries. As a means of scalability, Panda smartly precomputes parts of the most frequent incoming predictive queries, which significantly reduces the query response time. Panda employs a tunable threshold that achieves a trade-off between query response time and the maintenance cost of precomptued answers. Experimental results, based on large data sets, show that Panda is scalable, efficient, and as accurate as its underlying prediction function.

Original languageEnglish
Title of host publication20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
Pages13-22
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012 - Redondo Beach, CA, United States
Duration: 6 Nov 20129 Nov 2012

Other

Other20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
CountryUnited States
CityRedondo Beach, CA
Period6/11/129/11/12

Fingerprint

Query
prediction
traffic management
trade-off
Scalability
Marketing
Response Time
Prediction
Traffic Management
Scale-up
cost
Moving Objects
Costs
Large Data Sets
Sharing
Maintenance
Trade-offs
Target
Experimental Results

Keywords

  • location-based services
  • predictive spatio-temporal queries

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Hendawi, A. M., & Mokbel, M. (2012). Panda: A predictive spatio-temporal query processor. In 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012 (pp. 13-22) https://doi.org/10.1145/2424321.2424324

Panda : A predictive spatio-temporal query processor. / Hendawi, Abdeltawab M.; Mokbel, Mohamed.

20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. p. 13-22.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hendawi, AM & Mokbel, M 2012, Panda: A predictive spatio-temporal query processor. in 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. pp. 13-22, 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012, Redondo Beach, CA, United States, 6/11/12. https://doi.org/10.1145/2424321.2424324
Hendawi AM, Mokbel M. Panda: A predictive spatio-temporal query processor. In 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. p. 13-22 https://doi.org/10.1145/2424321.2424324
Hendawi, Abdeltawab M. ; Mokbel, Mohamed. / Panda : A predictive spatio-temporal query processor. 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012. 2012. pp. 13-22
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