Time-series prediction with applications to traffic and moving objects databases
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
HERMES: aggregative LBS via a trajectory DB engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Hermes – a framework for location-based data management
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Frequent route based continuous moving object location- and density prediction on road networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hi-index | 0.00 |
In this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times. This paper proposes a prediction method which uses the local models extracted by Trajectory Pattern to build a global model called Prediction Tree. The future location of a moving object is predicted visiting the tree and calculating the best matching function. The integration within DAEDALUS system supports an interactive construction of the predictor on the top of a set of spatio-temporal patterns. Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information.