Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Top-k dominating queries in uncertain databases
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
Computing all skyline probabilities for uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Ranking distributed probabilistic data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient join processing on uncertain data streams
Proceedings of the 18th ACM conference on Information and knowledge management
Reverse skyline search in uncertain databases
ACM Transactions on Database Systems (TODS)
Probabilistic string similarity joins
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
K-nearest neighbor search for fuzzy objects
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A generic framework for handling uncertain data with local correlations
Proceedings of the VLDB Endowment
Finding the least influenced set in uncertain databases
Information Systems
Set similarity join on probabilistic data
Proceedings of the VLDB Endowment
Probabilistic inverse ranking queries in uncertain databases
The VLDB Journal — The International Journal on Very Large Data Bases
Context-sensitive document ranking
Journal of Computer Science and Technology
Asymptotically efficient algorithms for skyline probabilities of uncertain data
ACM Transactions on Database Systems (TODS)
Shooting top-k stars in uncertain databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient processing of probabilistic set-containment queries on uncertain set-valued data
Information Sciences: an International Journal
MUD: Mapping-based query processing for high-dimensional uncertain data
Information Sciences: an International Journal
Top-k similarity join over multi-valued objects
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Spatial query processing for fuzzy objects
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic top-k dominating queries in uncertain databases
Information Sciences: an International Journal
UV-diagram: a voronoi diagram for uncertain spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient top-k spatial distance joins
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Probabilistic data have recently become popular in applications such as scientific and geospatial databases. For images and other spatial datasets, probabilistic values can capture the uncertainty in extent and class of the objects in the images. Relating one such dataset to another by spatial joins is an important operation for data management systems. We consider probabilistic spatial join (PSJ) queries, which rank the results according to a score that incorporates both the uncertainties associated with the objects and the distances between them. We present algorithms for two kinds of PSJ queries: Threshold PSJ queries, which return all pairs that score above a given threshold, and top-k PSJ queries, which return the k top-scoring pairs. For threshold PSJ queries, we propose a plane sweep algorithm that, because it exploits the special structure of the problem, runs in O(n (log n + k)) time, where n is the number of points and k is the number of results. We extend the algorithms to 2-D data and to top-k PSJ queries. To further speed up top-k PSJ queries, we develop a scheduling technique that estimates the scores at the level of blocks, then hands the blocks to the plane sweep algorithm. By finding high-scoring pairs early, the scheduling allows a large portion of the datasets to be pruned. Experiments demonstrate speed-ups of two orders of magnitude.