%0 Journal Article %1 cios02trends %A Cios, K. %A Kurgan, L. %D 2002 %J Knowledge Discovery in Advanced Information Systems, Pal N.R., Jain, L.C. and Teoderesku, N. (Eds.) %K discovery Knowledge process, Overview DM CRIS %T Trends in Data Mining and Knowledge Discovery %U citeseer.ist.psu.edu/cios05trends.html %X Data Mining and Knowledge Discovery (DMKD) is one of the fast growing computer science fields. Its popularity is caused by an increased demand for tools that help with the analysis and understanding of huge amounts of data. Such data are generated on a daily basis by institutions like banks, insurance companies, retail stores, and on the Internet. This explosion came into being through the ever increasing use of computers, scanners, digital cameras, bar codes, etc. We are in a situation when rich sources of data, stored in databases, warehouses, and other data repositories, are readily available. This in turn causes big interest of business and industrial communities in the field of DMKD. What is needed is a clear and simple methodology for extracting the knowledge that is hidden in the data. In this chapter, an integrated DMKD process model based on the emerging technologies like XML, PMML, SOAP, UDDI, and OLE BD-DM is introduced. These technologies help designing flexible, semi-automated, and easy to use DMKD model. They enable the building of knowledge repositories. They allow for communication between several data mining tools, databases and knowledge repositories. They also enable integration and automation of DMKD tasks. The chapter describes a six-step DMKD process model, the above mentioned technologies, and their implementation details.

@article{cios02trends, abstract = {Data Mining and Knowledge Discovery (DMKD) is one of the fast growing computer science fields. Its popularity is caused by an increased demand for tools that help with the analysis and understanding of huge amounts of data. Such data are generated on a daily basis by institutions like banks, insurance companies, retail stores, and on the Internet. This explosion came into being through the ever increasing use of computers, scanners, digital cameras, bar codes, etc. We are in a situation when rich sources of data, stored in databases, warehouses, and other data repositories, are readily available. This in turn causes big interest of business and industrial communities in the field of DMKD. What is needed is a clear and simple methodology for extracting the knowledge that is hidden in the data. In this chapter, an integrated DMKD process model based on the emerging technologies like XML, PMML, SOAP, UDDI, and OLE BD-DM is introduced. These technologies help designing flexible, semi-automated, and easy to use DMKD model. They enable the building of knowledge repositories. They allow for communication between several data mining tools, databases and knowledge repositories. They also enable integration and automation of DMKD tasks. The chapter describes a six-step DMKD process model, the above mentioned technologies, and their implementation details.}, added-at = {2006-07-16T10:28:56.000+0200}, author = {Cios, K. and Kurgan, L.}, biburl = {https://www.bibsonomy.org/bibtex/2c9cb9e3e69bb99926171bb6635b34d44/grani}, comment = {Provides a good overview for the KDP}, interhash = {e9fd15b8e89a17b2a46bc25d96aeba02}, intrahash = {c9cb9e3e69bb99926171bb6635b34d44}, journal = {Knowledge Discovery in Advanced Information Systems, Pal N.R., Jain, L.C. and Teoderesku, N. (Eds.)}, keywords = {discovery Knowledge process, Overview DM CRIS}, owner = {mgrani}, text = {Cios, K. J., & Kurgan, L. (2002), Trends in Data Mining and Knowledge Discovery, In: Pal N.R., Jain, L.C. and Teoderesku, N. (Eds.), Knowledge Discovery in Advanced Information Systems, Springer, in print}, timestamp = {2006-07-16T10:28:56.000+0200}, title = {Trends in Data Mining and Knowledge Discovery}, url = {citeseer.ist.psu.edu/cios05trends.html}, year = 2002 }