Abstract
Data Integration systems are used to integrate heterogeneous data sources in a single view. Recent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems. This require for method based on a semi-automatic procedure that can provide reliable results. A crucial factor for any semi automatic algorithm is based on the matching operators implemented. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account a variety of options.
This paper proposes a solution based on a categorization of marching operators that allow to group similar attributes on a semantic rich form. The validation of the system have demonstrate how the aggregation of matching operators is not a trivial problem because traditional aggregators produce a compensation effect on operators that can have very different informative values. For this reason this work is now evolving thought the implementation of aggregators based on logic theories, able to distinguish different properties of matching operators.
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OWL - Web Ontology Language defintion, http://www.w3.org/TR/owl-features/
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Ceravolo, P., Cui, Z., Damiani, E., Gusmini, A., Leida, M. (2008). ODDI: Ontology-Driven Data Integration. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_66
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DOI: https://doi.org/10.1007/978-3-540-85563-7_66
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