Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries
The VLDB Journal — The International Journal on Very Large Data Bases
Optimized algorithms for predictive range and KNN queries on moving objects
Information Systems
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Destination prediction by sub-trajectory synthesis and privacy protection against such prediction
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Destination prediction is an essential task for a number of emerging location based applications such as recommending sightseeing places and sending targeted advertisements. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques suffer from the "data sparsity problem", i.e., the number of available historical trajectories is far from sufficient to cover all possible trajectories. This problem considerably limits the amount of query trajectories whose predicted destinations can be inferred. In this demonstration, we showcase a system named "DesTeller" that is interactive, user-friendly, publicly accessible, and capable of answering real-time queries. The underlying algorithm Sub-Trajectory Synthesis (SubSyn) successfully addressed the data sparsity problem and is able to predict destinations for almost every query submitted by travellers. We also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users.