Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Selectivity estimation for spatio-temporal queries to moving objects
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing Animated Objects Using Spatiotemporal Access Methods
IEEE Transactions on Knowledge and Data Engineering
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
A Spatiotemporal Model and Language for Moving Objects on Road Networks
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Clustering moving objects for spatio-temporal selectivity estimation
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Performance evaluation of spatio-temporal selectivity estimation techniques
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Hi-index | 0.00 |
Recently, it has become possible to collect large amounts of trajectory data of moving objects by using sensor networks. To manage such trajectory data, we have developed a distributed trajectory database composed of a server and many sensor nodes deployed over wide areas. The server manages the trajectory data of each moving object by using indices. However, since each sensor node cannot send trajectory data to the server all the time, the server does not always manage indices for the current trajectory data. In other words, the server is delayed in answering queries for current data because it has to forward each query to the sensor nodes to answer them. This is defined as a delay problem. To avoid this problem, we propose a pattern-based predictive indexing method for the database to answer queries in real time. This method uses past motion patterns of moving objects to predict the future locations of moving objects. In this paper, we describe the method and evaluate it with practical trajectory data. We conclude that the technique can predict the future locations of moving objects well enough in real time and show optimal parameters for prediction.