Statistical analysis with missing data
Statistical analysis with missing data
On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Annals of Operations Research
Counting linear extensions is #P-complete
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Faster random generation of linear extensions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Learning missing values from summary constraints
ACM SIGKDD Explorations Newsletter
Incomplete Relational Database Models Based on Intervals
IEEE Transactions on Knowledge and Data Engineering
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Multidimensional Integration: Partition and Conquer
Computing in Science and Engineering
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Progressive and selective merge: computing top-k with ad-hoc ranking functions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Efficient Skyline and Top-k Retrieval in Subspaces
IEEE Transactions on Knowledge and Data Engineering
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Bayesian method for guessing the extreme values in a data set?
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Query processing over incomplete autonomous databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
On the semantics and evaluation of top-k queries in probabilistic databases
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Consensus answers for queries over probabilistic databases
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
Building ranked mashups of unstructured sources with uncertain information
Proceedings of the VLDB Endowment
Search computing
Ranking with uncertain scoring functions: semantics and sensitivity measures
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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
Context-aware top-K processing using views
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes introduces new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records' scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. In this paper, we present a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty. Under this model, we formulate several ranking query types with different semantics. We describe and analyze a set of efficient query evaluation algorithms. We show that our techniques can be used to solve the problem of rank aggregation in partial orders under two widely adopted distance metrics. In addition, we design sampling techniques based on Markov chains to compute approximate query answers. Our experimental evaluation uses both real and synthetic data. The experimental study demonstrates the efficiency and effectiveness of our techniques under various configurations.