The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Modern Information Retrieval
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Method for Photograph Indexing Using Speech Annotation
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Practical fast polynomial multiplication
SYMSAC '76 Proceedings of the third ACM symposium on Symbolic and algebraic computation
Evaluating different methods of estimating retrieval quality for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Robust and efficient fuzzy match for online data cleaning
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
Selecting effective index terms using a decision tree
Natural Language Engineering
Optimizing Top-k Selection Queries over Multimedia Repositories
IEEE Transactions on Knowledge and Data Engineering
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic score estimation with piecewise logistic regression
ICML '04 Proceedings of the twenty-first international conference on Machine learning
High precision extraction of grammatical relations
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Domain-independent data cleaning via analysis of entity-relationship graph
ACM Transactions on Database Systems (TODS)
Index for fast retrieval of uncertain spatial point data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Adaptive graphical approach to entity resolution
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A unified approach for schema matching, coreference and canonicalization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Toward Managing Uncertain Spatial Information for Situational Awareness Applications
IEEE Transactions on Knowledge and Data Engineering
Fast and Simple Relational Processing of Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Using Semantics for Speech Annotation of Images
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Consensus answers for queries over probabilistic databases
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Exploiting context analysis for combining multiple entity resolution systems
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
XAR: An Integrated Framework for Information Extraction
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
Self-tuning in graph-based reference disambiguation
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A Semantics-Based Approach for Speech Annotation of Images
IEEE Transactions on Knowledge and Data Engineering
Exploiting Web querying for Web people search
ACM Transactions on Database Systems (TODS)
Adaptive Connection Strength Models for Relationship-Based Entity Resolution
Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
Super-EGO: fast multi-dimensional similarity join
The VLDB Journal — The International Journal on Very Large Data Bases
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Modern data processing techniques such as entity resolution, data cleaning, information extraction, and automated tagging often produce results consisting of objects whose attributes may contain uncertainty. This uncertainty is frequently captured in the form of a set of multiple mutually exclusive value choices for each uncertain attribute along with a measure of probability for alternative values. However, the lay end-user, as well as some end-applications, might not be able to interpret the results if outputted in such a form. Thus, the question is how to present such results to the user in practice, for example, to support attribute-value selection and object selection queries the user might be interested in. Specifically, in this article we study the problem of maximizing the quality of these selection queries on top of such a probabilistic representation. The quality is measured using the standard and commonly used set-based quality metrics. We formalize the problem and then develop efficient approaches that provide high-quality answers for these queries. The comprehensive empirical evaluation over three different domains demonstrates the advantage of our approach over existing techniques.