Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Path prediction is presently an important area of research with a wide range of applications. However, most of the existing path prediction solutions are based on eager learning methods which commit to a model or a set of patterns extracted from historical trajectories. Such methods do not perform very well in dynamic environments where the objects' trajectories are affected by many irregular factors which are not captured by pre-defined models or patterns. In this demonstration, we present the "R2-D2" system that supports probabilistic path prediction in dynamic environments. The core of our system is a "semi-lazy" learning approach to probabilistic path prediction which builds a prediction model on the fly using historical trajectories that are selected dynamically based on the trajectories of target objects. Our "R2-D2" system has a visual interface that shows how our path prediction algorithm works on several real-world datasets. It also allows us to experiment with various parameter settings.