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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fayyad, U.M.; Djorgovski, S.G.: Automating the Analysis and Cataloging of Sky Surveys. Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1.996: 471–493
Graefe, G.: Volcano, an Extensible and Parallel Dataflow Query Processing System. IEEE Trans. on Knowledge and Data Eng., 1.994: 120–135
Holsheimer, M.; Kersten, M.L.: Architectural Support for Data Mining. Technical Report, CWI, Number CS-R9429, 1.994
Menasalvas, E.: Integrating Relational Databases and KDD Process: Mathematical Modelization of the Data Mining Step of the Process. Phd Thesis, disserted Politachnical University (UPM), Spain, 1.998
Matheus, C.J.; Piatesky-Shaphiro, G.: Selecting and Reporting What is Interesting: The KEFIR Application to Heatlhcare Data. Advances in Knowledge discovery and Data Mining, AAAI/MIT Press, 1.996: 399–421
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-48298-9_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66458-1
Online ISBN: 978-3-540-48298-7
eBook Packages: Springer Book Archive