Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
CCS expressions, finite state processes, and three problems of equivalence
PODC '83 Proceedings of the second annual ACM symposium on Principles of distributed computing
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient reasoning in graphical models
Efficient reasoning in graphical models
Learning probabilistic models of link structure
The Journal of Machine Learning Research
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Machine Learning
Clean Answers over Dirty Databases: A Probabilistic Approach
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Creating probabilistic databases from information extraction models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Model-driven data acquisition in sensor networks
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
Representing Tuple and Attribute Uncertainty in Probabilistic Databases
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Exploiting shared correlations in probabilistic databases
Proceedings of the VLDB Endowment
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Bisimulation-based approximate lifted inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Handling uncertainty and ignorance in databases: a rule to combine dependent data
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Extending factor graphs so as to unify directed and undirected graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
About projection-selection-join queries addressed to possibilistic relational databases
IEEE Transactions on Fuzzy Systems
PrDB: Managing Large-Scale Correlated Probabilistic Databases (Abstract)
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
Increasing representational power and scaling reasoning in probabilistic databases
Proceedings of the 13th International Conference on Database Theory
Probabilistic string similarity joins
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Bayesian knowledge corroboration with logical rules and user feedback
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Combining intensional with extensional query evaluation in tuple independent probabilistic databases
Information Sciences: an International Journal
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
Read-once functions and query evaluation in probabilistic databases
Proceedings of the VLDB Endowment
Incrementally maintaining classification using an RDBMS
Proceedings of the VLDB Endowment
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Sensitivity analysis and explanations for robust query evaluation in probabilistic databases
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
The monte carlo database system: Stochastic analysis close to the data
ACM Transactions on Database Systems (TODS)
A truly dynamic data structure for top-k queries on uncertain data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Database foundations for scalable RDF processing
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Interactive reasoning in uncertain RDF knowledge bases
Proceedings of the 20th ACM international conference on Information and knowledge management
Local structure and determinism in probabilistic databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
H-Tree: a hybrid structure for confidence computation in probabilistic databases
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
Xtream: a system for continuous querying over uncertain data streams
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Towards high-throughput gibbs sampling at scale: a study across storage managers
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Lifted variable elimination: decoupling the operators from the constraint language
Journal of Artificial Intelligence Research
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Due to numerous applications producing noisy data, e.g., sensor data, experimental data, data from uncurated sources, information extraction, etc., there has been a surge of interest in the development of probabilistic databases. Most probabilistic database models proposed to date, however, fail to meet the challenges of real-world applications on two counts: (1) they often restrict the kinds of uncertainty that the user can represent; and (2) the query processing algorithms often cannot scale up to the needs of the application. In this work, we define a probabilistic database model, PrDB, that uses graphical models, a state-of-the-art probabilistic modeling technique developed within the statistics and machine learning community, to model uncertain data. We show how this results in a rich, complex yet compact probabilistic database model, which can capture the commonly occurring uncertainty models (tuple uncertainty, attribute uncertainty), more complex models (correlated tuples and attributes) and allows compact representation (shared and schema-level correlations). In addition, we show how query evaluation in PrDB translates into inference in an appropriately augmented graphical model. This allows us to easily use any of a myriad of exact and approximate inference algorithms developed within the graphical modeling community. While probabilistic inference provides a generic approach to solving queries, we show how the use of shared correlations, together with a novel inference algorithm that we developed based on bisimulation, can speed query processing significantly. We present a comprehensive experimental evaluation of the proposed techniques and show that even with a few shared correlations, significant speedups are possible.