TY - GEN
T1 - Panda
T2 - 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
AU - Hendawi, Abdeltawab M.
AU - Mokbel, Mohamed F.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - location-based services
KW - predictive spatio-temporal queries
UR - http://www.scopus.com/inward/record.url?scp=84872799934&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872799934&partnerID=8YFLogxK
U2 - 10.1145/2424321.2424324
DO - 10.1145/2424321.2424324
M3 - Conference contribution
AN - SCOPUS:84872799934
SN - 9781450316910
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 13
EP - 22
BT - 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
Y2 - 6 November 2012 through 9 November 2012
ER -