In the past, research in data mining has mainly been concerned with small to moderately sized data sets and knowledge-weak domains with focus on largely homogeneous and localized computing environments. These assumptions are no longer met in modern scientific and industrial complex problem-solving environments, which are increasingly relying on the sharing of geographically dispersed computing resources. Such knowledge-based sectors are characterized by an ever-increasing amount of digital data, information, and knowledge generated by the underlying activities and processes.

Therefore, future knowledge discovery applications will need to operate on very large and distributed data sets and against highly structured and complex domain knowledge available in digital form. The data sets, the domain knowledge, and the programs for processing, analyzing, evaluating and visualizing these data, and other relevant resources will increasingly reside at geographically distributed sites on heterogeneous infrastructures and platforms (e.g. operating systems, hardware and software architectures and systems). Therefore, the shift to large-scale distributed computing is likely to have profound implications in terms of the ways how data are managed, shared and analyzed. Grid technology is expected to provide the necessary middle-tier layer in future distributed data mining computing environments.

The goal of the DataMiningGrid project is to develop a coherent environment for development and use of data mining applications in current grid environments.

The DataMiningGrid Consortium consists of 5 partners from 4 countries. The Faculty of Civil and Geodetic Engineering, Department for Construction Informatics contributes to this project in the process of collection and evaluation of user requirements, leads the system design as well as the demonstrations workpackage.

The project will last for one more year. More information at http://www.datamininggrid.org