Indexing and querying moving objects with uncertain speed and direction in spatiotemporal databases

Efficient processing of spatiotemporal queries over moving objects with uncertainty has become imperative due to the increasing need for real-time information in highly dynamic environments. Most of the existing approaches focus on designing an index structure for managing moving objects with uncertainty and then utilize it to improve the query performance. All the proposed indexes, however, have their own limitations. In this paper, we devote to developing an efficient index, named the R (lsd) -tree, to index moving objects with uncertain speed and direction varying within respective known ranges. We design several pruning criteria combined with the R (lsd) -tree to answer the probabilistic range queries. Moreover, two models, the sampling-based probability model and the ER-based probability model, are proposed to quantify the possibility of each object being the query result. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.