The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
The Geometry of Uncertainty in Moving Objects Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Effectively indexing uncertain moving objects for predictive queries
Proceedings of the VLDB Endowment
Trajectory databases: Data models, uncertainty and complete query languages
Journal of Computer and System Sciences
Uncertain Range Queries for Necklaces
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Effectively Indexing the Uncertain Space
IEEE Transactions on Knowledge and Data Engineering
Discovering popular routes from trajectories
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Querying Uncertain Spatio-Temporal Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Minimal spatio-temporal database repairs
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The advances in sensing and telecommunication technologies allow the collection and management of vast amounts of spatio-temporal data combining location and time information.Due to physical and resource limitations of data collection devices (e.g., RFID readers, GPS receivers and other sensors) data are typically collected only at discrete points of time. In-between these discrete time instances, the positions of tracked moving objects are uncertain. In this work, we propose novel approximation techniques in order to probabilistically bound the uncertain movement of objects; these techniques allow for efficient and effective filtering during query evaluation using an hierarchical index structure.To the best of our knowledge, this is the first approach that supports query evaluation on very large uncertain spatio-temporal databases, adhering to possible worlds semantics. We experimentally show that it accelerates the existing, scan-based approach by orders of magnitude.