Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Volcano— An Extensible and Parallel Query Evaluation System
IEEE Transactions on Knowledge and Data Engineering
Architectural Support for Data Mining.
Architectural Support for Data Mining.
Extension of Relational Management Systems with Data Mining Capabilities
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
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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.