Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Proceedings of the 17th International Conference on Data Engineering
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
GADT: A Probability Space ADT for Representing and Querying the Physical World
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Stabbing the Sky: Efficient Skyline Computation over Sliding Windows
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Answering queries from statistics and probabilistic views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The Gauss-Tree: Efficient Object Identification in Databases of Probabilistic Feature Vectors
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Integrating wireless EEGs into medical sensor networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Continuous Skyline Queries for Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
Skyline Index for Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Detecting sensor faults for a chemical reactor rig via adaptive neural network model
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Selective data acquisition for probabilistic K-NN query
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information Sciences: an International Journal
Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods
Journal of Intelligent Information Systems
Skyline queries in crowd-enabled databases
Proceedings of the 16th International Conference on Extending Database Technology
Parallel skyline queries over uncertain data streams in cloud computing environments
International Journal of Web and Grid Services
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The ability to deal with uncertain information is becoming increasingly important for modern database applications. Whereas a conventional (certain) object is usually represented by a vector from a multidimensional feature space, an uncertain object is represented by a multivariate probability density function (PDF). This PDF can be defined either discretely (e.g. by a histogram) or continuously in parametric form (e.g. by a Gaussian Mixture Model). For a database of uncertain objects, the users expect similar data analysis techniques as for a conventional database of certain objects. An important analysis technique for certain objects is the skyline operator which finds maximal or minimal vectors with respect to any possible attribute weighting. In this paper, we propose the concept of probabilistic skylines, an extension of the skyline operator for uncertain objects. In addition, we propose efficient and effective methods for determining the probabilistic skyline of uncertain objects which are defined by a PDF in parametric form (e.g. a Gaussian function or a Gaussian Mixture Model). To further accelerate the search, we elaborate how the computation of the probabilistic skyline can be supported by an index structure for uncertain objects. An extensive experimental evaluation demonstrates both the effectiveness and the efficiency of our technique.