Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Query processing in the SIMS information mediator
Readings in agents
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Using ontologies for XML data cleaning
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
Interchangeable consistency constraints for public health care systems
Proceedings of the 2010 ACM Symposium on Applied Computing
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Automatic detection and removal of inconsistencies in data are open challenges in the data quality management cycle. Specific knowledge is needed to clean invalid data, which often requires user interaction with domain experts. Domain specific classes and attributes can be described in ontologies. Attribute value combinations can be labeled as valid or invalid. Our approach on data cleaning allows for detection and removal of semantic errors in data. The analysis of replacements enables the creation of rules, which can minimize the required user interaction. We provide an algorithm which analyzes frequencies of replacement operations for invalid tuples in the ontology and generates rules, which are then applied in data cleaning environments automatically.