Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Detecting region outliers in meteorological data
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Monitoring dynamic spatial fields using responsive geosensor networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
Modeling and Predicting Future Trajectories of Moving Objects in a Constrained Network
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Contour map matching for event detection in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Distributed Real-Time Detection and Tracking of Homogeneous Regions in Sensor Networks
RTSS '06 Proceedings of the 27th IEEE International Real-Time Systems Symposium
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Modeling historical and future movements of spatio-temporal objects in moving objects databases
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Detection and Exploration of Outlier Regions in Sensor Data Streams
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
| Hi-index | 0.00 |
Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet. In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.