Predictive data mining: a practical guide
Predictive data mining: a practical guide
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Mapping between Relational Database Schema and OWL Ontology for Deep Annotation
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
OntoDataClean: ontology-based integration and preprocessing of distributed data
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
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Over the last years, collaborative research has been continuously growing in many scientific areas such as biomedicine. However, traditional Knowledge Discovery in Databases (KDD) processes generally adopt centralized approaches that do not fully address many research needs in these distributed environments. This paper presents a method to improve traditional centralized KDD by adopting an ontology-based distributed model. Ontologies are used within this model: (i) as Virtual Schemas (VS) to solve structural heterogeneities in databases and (ii) as frameworks to guide automatic transformations when data is retrieved by users--Preprocessing Ontologies (PO). Both types of ontologies aim to facilitate data gathering and preprocessing while maintaining data source decentralization. This ontology-based approach allows to link database integration and data mining, improving final results, reusability and interoperability. The results obtained present improvements in outcome performance and new capabilities compared to traditional KDD processes.