Lineage tracing for general data warehouse transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Practical Lineage Tracing in Data Warehouses
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference 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
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Querying continuous functions in a database system
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Monte-Carlo algorithms for enumeration and reliability problems
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Probabilistic databases: diamonds in the dirt
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
The monte carlo database system: Stochastic analysis close to the data
ACM Transactions on Database Systems (TODS)
Probabilistic threshold join over distributed uncertain data
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Efficient and scalable monitoring and summarization of large probabilistic data
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
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MCDB is a prototype database system for managing stochastic models for uncertain data. In this paper, we study the problem of how to use MCDB to answer statistical queries that search for database objects which satisfy some filter condition with greater (or less than) a user-specified probability. For example: "Which packages will arrive late with 5% probability?" "Which regions will see more than a 2% decline in sales with 50% probability?" "What items will be out of stock by Friday with 20% probability?" We consider both the systems aspects and the statistical aspects of the problem.