Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems
ACM Transactions on Database Systems (TODS)
A probabilistic relational data model
EDBT '90 Proceedings of the 2nd international conference on extending database technology: Advances in Database Technology
New direction for uncertainty reasoning in deductive databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
A probabilistic relational model for the integration of IR and databases
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic relational model and algebra
ACM Transactions on Database Systems (TODS)
A statistical approach to incomplete information in database systems
ACM Transactions on Database Systems (TODS)
A Probability Model of Uncertainty in Data Bases
Proceedings of the Second International Conference on Data Engineering
The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
An Extended Relational Database Model for Uncertain and Imprecise Information
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Modeling Uncertainty in Deductive Databases
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
Querying structured text in an XML database
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
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In many applications, uncertainty and ignorance go hand in hand. Therefore, to deliver database support for effective decision making, an integrated view of uncertainty and ignorance should be taken. So far, most of the efforts attempted to capture uncertainty and ignorance with probability theory. In this paper, we discuss the weakness to capture ignorance with probability theory, and propose an approach inspired by the Dempster-Shafer theory to capture uncertainty and ignorance. Then, we present a rule to combine dependent data that are represented in different relations. Such a rule is required to perform joins in a consistent way. We illustrate that our rule is able to solve the so-called problem of information loss, which was considered as an open problem so far.