Effective density queries for moving objects in road networks

Authors:
Caifeng Lai;Ling Wang;Jidong Chen;Xiaofeng Meng;Karine Zeitouni
Affiliations:
School of Information, Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE;School of Information, Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE;School of Information, Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE;School of Information, Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE;PRISM, Versailles Saint-Quentin University, France
Venue:
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Year:
2007

Citing 10
Cited 4

Algorithms for clustering data

Algorithms for clustering data
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
Clustering objects on a spatial network

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Clustering moving objects

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregate Nearest Neighbor Queries in Road Networks

IEEE Transactions on Knowledge and Data Engineering
An efficient and scalable approach to CNN queries in a road network

VLDB '05 Proceedings of the 31st international conference on Very large data bases
Reverse Nearest Neighbors in Large Graphs

IEEE Transactions on Knowledge and Data Engineering
Effective Density Queries on ContinuouslyMoving Objects

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Continuous nearest neighbor monitoring in road networks

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Query processing in spatial network databases

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

Continuous Clustering of Moving Objects in Spatial Networks

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Caractérisation de la densité de trafic et de son évolution à partir de trajectoires d'objets mobiles

Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
Optimizing predictive queries on moving objects under road-network constraints

DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Predictive line queries for traffic prediction

Transactions on Large-Scale Data- and Knowledge-Centered Systems VI

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent research has focused on density queries for moving objects in highly dynamic scenarios. An area is dense if the number of moving objects it contains is above some threshold. Monitoring dense areas has applications in traffic control systems, bandwidth management, collision probability evaluation, etc. All existing methods, however, assume the objects moving in the Euclidean space. In this paper, we study the density queries in road networks, where density computation is determined by the length of the road segment and the number of objects on it. We define an effective road-network density query guaranteeing to obtain useful answers. We then propose the cluster-based algorithm for the efficient computation of density queries for objects moving in road networks. Extensive experimental results show that our methods achieve high efficiency and accuracy for finding the dense areas in road networks.