On the representation and querying of sets of possible worlds
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Enterprise information integration: successes, challenges and controversies
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Data integration with uncertainty
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic databases: diamonds in the dirt
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
Fast and Simple Relational Processing of Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Information integration with uncertainty
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
A Survey on Uncertainty Management in Data Integration
Journal of Data and Information Quality (JDIQ)
Foundations of uncertain-data integration
Proceedings of the VLDB Endowment
Beauty and the beast: the theory and practice of information integration
ICDT'07 Proceedings of the 11th international conference on Database Theory
On the foundations of probabilistic information integration
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
The probabilistic relation model has been used for the compact representation of uncertain data in relational databases. In this paper we present the extended probabilistic relation model, a compact representation for uncertain information that admits efficient information integration. We present an algorithm for data integration using this model and prove its correctness. We also explore the complexity of query evaluation under the probabilistic and extended probabilistic models. Finally, we study the problem of obtaining a (pure) probabilistic relation that is equivalent to a given extended probabilistic relation, and present approaches and algorithms for this task. This work is the first and critical step towards practical and efficient uncertain information integration.