Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining models as services on the internet
ACM SIGKDD Explorations Newsletter
Data mining standards initiatives
Communications of the ACM - Evolving data mining into solutions for insights
Communications of the ACM
IT Professional
Unraveling the Web Services Web: An Introduction to SOAP, WSDL, and UDDI
IEEE Internet Computing
The Open Grid Services Architecture: Where the Grid Meets the Web
IEEE Internet Computing
Workflow-Based Composition of Web-Services: A Business Model or a Programming Paradigm?
EDOC '02 Proceedings of the 6th International Enterprise Distributed Object Computing Conference
Integration of Web Services into Workflows through a Multi-Level chema Architecture
WECWIS '02 Proceedings of the Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS'02)
PaDDMAS: Parallel and Distributed Data Mining Application Suite
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Quantizing for minimum average misclassification risk
IEEE Transactions on Neural Networks
KDD support services based on data semantics
Journal on Data Semantics IV
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Knowledge Discovery in Databases (KDD) is a highly complex process where a lot of data manipulation tools with different characteristics can, and in fact have to, be used together in an interactive and iterative fashion, to reach the goal of previously unknown, potentially useful information extraction. In this paper we analyze the major sources of complexity in the framework of network organizations, pointing out the necessity to give support to the user in many different ways and at very different levels of granularity, from the use of a single tool, to the management of whole, distributed, KDD projects. Unfortunately, currently available systems lack to support the users in at least some of these features. We then propose a solution based on the Service Oriented Computing paradigm, arguing that the advantages of this paradigm, namely openness, modularity, reusability and transparency, as well as ubiquity, can help in the design of an effective support system for Knowledge Discovery in Databases in network environments.