Data structures and algorithms 3: multi-dimensional searching and computational geometry
Data structures and algorithms 3: multi-dimensional searching and computational geometry
Computational geometry: an introduction
Computational geometry: an introduction
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Proceedings of the 17th International Conference on Data Engineering
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
URank: formulation and efficient evaluation of top-k queries in uncertain databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient skyline computation over low-cardinality domains
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient computation of reverse skyline queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient Skyline Retrieval on Peer-to-Peer Networks
FGCN '07 Proceedings of the Future Generation Communication and Networking - Volume 02
Probabilistic ranked queries in uncertain databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Monochromatic and bichromatic reverse skyline search over uncertain databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Fast and Simple Relational Processing of Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Top-k Spatial Joins of Probabilistic Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Skyline Operator over Sliding Windows
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Continuous probabilistic skyline queries over uncertain data streams
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Threshold rules for online sample selection
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
Identifying interesting instances for probabilistic skylines
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
(Approximate) uncertain skylines
Proceedings of the 14th International Conference on Database Theory
Asymptotically efficient algorithms for skyline probabilities of uncertain data
ACM Transactions on Database Systems (TODS)
Categorical data skyline using classification tree
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Information Sciences: an International Journal
Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods
Journal of Intelligent Information Systems
Path skyline for moving objects
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Top-k best probability queries on probabilistic data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Skyline queries on keyword-matched data
Information Sciences: an International Journal
Skyline queries in crowd-enabled databases
Proceedings of the 16th International Conference on Extending Database Technology
Probabilistic skyline operator over sliding windows
Information Systems
Entity resolution for distributed probabilistic data
Distributed and Parallel Databases
Top-k best probability queries and semantics ranking properties on probabilistic databases
Data & Knowledge Engineering
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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Skyline computation is widely used in multi-criteria decision making. As research in uncertain databases draws increasing attention, skyline queries with uncertain data have also been studied, e.g. probabilistic skylines. The previous work requires "thresholding" for its efficiency -- the efficiency relies on the assumption that points with skyline probabilities below a certain threshold can be ignored. But there are situations where "thresholding" is not desirable -- low probability events cannot be ignored when their consequences are significant. In such cases it is necessary to compute skyline probabilities of all data items. We provide the first algorithm for this problem whose worst-case time complexity is sub-quadratic. The techniques we use are interesting in their own right, as they rely on a space partitioning technique combined with using the existing dominance counting algorithm. The effectiveness of our algorithm is experimentally verified.