Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Performance Analysis of Location-Dependent Cache Invalidation Schemes for Mobile Environments
IEEE Transactions on Knowledge and Data Engineering
Dynamic Queries over Mobile Objects
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Semantic Caching in Location-Dependent Query Processing
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Effective Density Queries on ContinuouslyMoving Objects
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Challenges in spatiotemporal stream query optimization
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Monitoring dense areas, where the density of moving objects is higher than the given threshold, has many applications like traffic control, bandwidth management, and collision probability evaluation. Although many studies have been done on density queries for moving objects in highly dynamic scenarios, they all focused on how to answer snapshot density queries. In this paper, we focus on continuously monitoring dense regions for moving objects. Based on the notion of safe interval, we propose effective algorithms to evaluate and keep track of dense regions. Experimental results show that our method can achieve high efficiency when monitoring dense regions for moving objects.