K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Semantic Caching in Location-Dependent Query Processing
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Convex Optimization
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
K-nearest neighbor search for fuzzy objects
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Predictive spatio-temporal queries: a comprehensive survey and future directions
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as the constantly-changing nature of the external environment. Ignoring data uncertainty can render low query quality, and cause undesirable consequences such as missing alerts of threats and poor response time in rescue operations. Also, the query is quite time-consuming, since all the points on the trajectory are considered. In this paper, we study how to efficiently evaluate trajectory queries over imprecise location data, by proposing a new concept called the u-bisector. In general, the u-bisector is an extension of bisector to handle imprecise data. Based on the u-bisector, we design several novel filters to make our solution scalable to a long trajectory and a large database size. An extensive experimental study on real datasets suggests that our proposal produces better results than traditional solutions that do not consider data imprecision.