Abstract
Since KDD first appeared the research has been mainly focused on the development of efficient algorithms to extract hidden knowledge. As a result, a lot of systems have been implemented during the last decade. A common feature of these systems is that they either implement a specific algorithm or they are specific for a certain domain. As new algorithms are designed, existing systems have to be adapted, which means both redesigning and recompiling. Consequently, there is an urgent need to design and implement systems in which adding new algorithms or enhancing existing ones does not require recompiling and/or redesigning the whole system. In this paper we present the design and implementation of DAMISYS (DAta MIning SYStem). The innovative factor of DAMISYS is that it is an engine of KDD algorithms which means that it is able to run different algorithms that are loaded dynamicly during runtime. Another important feature of the system is that it makes possible to interact with any Data Warehouse, due to the connection subsytem that has been added.
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© 1999 Springer-Verlag Berlin Heidelberg
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Fernández, M.C. et al. (1999). DAMISYS: An Overview. In: Mohania, M., Tjoa, A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_33
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DOI: https://doi.org/10.1007/3-540-48298-9_33
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