Abstract
The problem of exchanging data, even considering incomplete and heterogeneous data, has been deeply investigated in the last years. The approaches proposed so far are quite rigid as they refer to fixed schema and/or are based on a deductive approach consisting in the use of a fixed set of (mapping) rules. In this paper, we propose HIKE (Highly Intelligent Knowledge Extraction), a framework that addresses this problem. The core of the framework consists of a smart data exchange architecture integrating deductive and inductive techniques to obtain new knowledge. The use of graph-based representation of source and target data, together with the midway relational database and the extraction of new knowledge allow us to manage dynamic databases where also features of data may change over the time. The paper also addresses the problem of computing certain answers in the new setting and reports a precise analysis of its complexity.
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Notes
- 1.
Clearly the shown snapshot is very small and it is reported just to give an idea of the type of data, but the technique works for relatively large datasets.
- 2.
We recall that the predicate name can be obtained by using ad-hoc wrappers and domain based ontologies.
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Acknowledgements
Authors have been supported by D-ALL and ProtectID projects, Elio Masciari has been supported by POR FESR Campania project Remiam.
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Greco, S., Masciari, E., Saccà, D., Trubitsyna, I. (2019). HIKE: A Step Beyond Data Exchange. In: Laender, A., Pernici, B., Lim, EP., de Oliveira, J. (eds) Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11788. Springer, Cham. https://doi.org/10.1007/978-3-030-33223-5_35
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