Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining for patterns in contradictory data
Proceedings of the 2004 international workshop on Information quality in information systems
Extending dependencies with conditions
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Conditional functional dependencies for capturing data inconsistencies
ACM Transactions on Database Systems (TODS)
Dependencies revisited for improving data quality
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On generating near-optimal tableaux for conditional functional dependencies
Proceedings of the VLDB Endowment
Discovering data quality rules
Proceedings of the VLDB Endowment
Unary and n-ary inclusion dependency discovery in relational databases
Journal of Intelligent Information Systems
Analyses and Validation of Conditional Dependencies with Built-in Predicates
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
When owl: sameAs isn't the same: an analysis of identity in linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Discovering Conditional Functional Dependencies
IEEE Transactions on Knowledge and Data Engineering
Extending inclusion dependencies with conditions
Theoretical Computer Science
ACM SIGMOD Record
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Data dependencies are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. Conditional dependencies have been introduced to analyze and improve data quality. A conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs).We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case.