Abstract
We are concerned with the processing of data held in distributed heterogeneous databases using domain knowledge, in the form of rules representing high-level knowledge about the data. This process facilitates the handling of missing, conflicting or unacceptable outlying data. In addition, by integrating the processed distributed data, we are able to extract new knowledge at a finer level of granularity than was present in the original data. Once integration has taken place the extracted knowledge, in the form of probabilities, may be used to learn association rules or Bayesian belief networks. Issues of confidentiality and efficiency of transfer of data across networks, whether the Internet or Intranets, are handled by aggregating the native data in situ, typically behind a firewall, and carrying out further transportation and processing solely on multidimensional aggregate tables. Heterogeneity is resolved by utilisation of domain knowledge for harmonisation and integration of the distributed data sources. Integration is carried out by minimisation of the Kullback-Leibler information divergence between the target integrated aggregates and the distributed data values.
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McClean, S., Scotney, B., Shapcott, M. (2004). Using Domain Knowledge to Learn from Heterogeneous Distributed Databases. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_28
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DOI: https://doi.org/10.1007/978-3-540-30132-5_28
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