Distributed Data Mining

Introduction

The continuous developments in information and communication technology have recently led to the appearance of distributed computing environments, which comprise several, and different sources of large volumes of data and several computing units. The most prominent example of a distributed environment is the Internet, where increasingly more databases and data streams appear that deal with several areas, such as meteorology, oceanography, economy and others. In addition the Internet constitutes the communication medium for geographically distributed information systems, as for example the earth observing system of NASA.

The application of the classical knowledge discovery process in distributed environments requires the collection of distributed data in a data warehouse for central processing. However, this is usually either ineffective or infeasible because of the storage, communication and computational cost, as well as the privacy issues involved in such an approach. Distributed Data Mining offers algorithms, methods and systems that deal with the above issues in order to discover knowledge from distributed data in an effective and efficient way.

Publications

• G. Tsoumakas and I. Vlahavas, " Distributed Data Mining of Large Classifier Ensembles ", Proc. (Companion Volume) 2nd Hellenic Conference on AI (SETN 2002), pp. 249-255, Thessaloniki, April 2002.
• G. Tsoumakas and I. Vlahavas, " Effective Stacking of Distributed Classifiers ", Proc. 15th European Conference on Artificial Intelligence (ECAI 2002), IOS Press, pp. 340-344, Lyon, France, July 2002.
• G. Tsoumakas, N. Bassiliades, I. Vlahavas, " A Knowledge-based Web Information System for the Federation of Distributed Classifiers ", in "Web Information Systems", D. Taniar and W. Rahayu (eds.), Ch. 8, pp. 271-308, Idea Group Publishing, 2004.
• G. Tsoumakas, L. Angelis and I. Vlahavas, " Clustering Classifiers for Knowledge Discovery from Physically Distributed Databases ", Data & Knowledge Engineering, 49(3), pp. 223-242, Elsevier, June 2004.
• G. Tsoumakas, I. Vlahavas, " A Scalable and Interoperable System for Classifier Sharing and Fusion ", Expert Systems with Applications, 33(3), (accepted for publication), Elsevier, November 2007.
• G. Tsoumakas, I. Vlahavas, " Distributed Data Mining" , in Encyclopedia of Data Warehousing and Mining - 2nd Edition, John Wang (Ed), (accepted for publication).

Links

• DDM Wiki