Answering heterogeneous database queries with degrees of uncertainty
Distributed and Parallel Databases
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Estimating the Quality of Databases
FQAS '98 Proceedings of the Third International Conference on Flexible Query Answering Systems
Completeness of integrated information sources
Information Systems - Special issue: Data quality in cooperative information systems
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
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Some data models use so-called maybe tuples to express the uncertainty, whether or not a tuple belongs to a relation. In order to assess this relation's quality the corresponding vagueness needs to be taken into account. Current metrics of quality dimensions are not designed to deal with this uncertainty and therefore need to be adapted. One major quality dimension is data completeness. In general, there are two basic ways to distinguish maybe tuples from definite tuples . First, an attribute serving as a maybe indicator (values YES or NO) can be used. Second, tuple probabilities can be specified. In this paper, the notion of data completeness is redefined w.r.t. both concepts. Thus, a more precise estimating of data quality in databases with maybe tuples (e.g. probabilistic databases) is enabled.