TY - GEN
T1 - Predictive query processing on moving objects
AU - Hendawi, Abdeltawab M.
AU - Mokbel, Mohamed F.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - A fundamental category of location based services relies on predictive queries which consider the anticipated future locations of users. Predictive queries attracted the researchers' attention as they are widely used in several applications including traffic management, routing, location-based advertising, and ride sharing. This paper aims to present a generic and scalable system for predictive query processing on moving objects, e.g, vehicles. Inside the proposed system, two frameworks are provided to work in two different environments, (1) Panda framework for euclidean space, and (2) iRoad framework for road network. Unlike previous work in supporting predictive queries, the target of the proposed system is to: (a) support long-term query prediction as well as short term prediction, (b) scale up to large number of moving objects, and (c) efficiently support different types of predictive queries, e.g., predictive range, KNN, and aggregate queries.
AB - A fundamental category of location based services relies on predictive queries which consider the anticipated future locations of users. Predictive queries attracted the researchers' attention as they are widely used in several applications including traffic management, routing, location-based advertising, and ride sharing. This paper aims to present a generic and scalable system for predictive query processing on moving objects, e.g, vehicles. Inside the proposed system, two frameworks are provided to work in two different environments, (1) Panda framework for euclidean space, and (2) iRoad framework for road network. Unlike previous work in supporting predictive queries, the target of the proposed system is to: (a) support long-term query prediction as well as short term prediction, (b) scale up to large number of moving objects, and (c) efficiently support different types of predictive queries, e.g., predictive range, KNN, and aggregate queries.
UR - http://www.scopus.com/inward/record.url?scp=84901782185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901782185&partnerID=8YFLogxK
U2 - 10.1109/ICDEW.2014.6818352
DO - 10.1109/ICDEW.2014.6818352
M3 - Conference contribution
AN - SCOPUS:84901782185
SN - 9781479934805
T3 - Proceedings - International Conference on Data Engineering
SP - 340
EP - 344
BT - 2014 IEEE 30th International Conference on Data Engineering Workshops, ICDEW 2014
PB - IEEE Computer Society
T2 - 2014 IEEE 30th International Conference on Data Engineering Workshops, ICDEW 2014
Y2 - 31 March 2014 through 4 April 2014
ER -
