Demonstrators and testing of the DataMiningGrid components is done on the basis of a selected set of real-world applications from a diverse set of sectors (see figure below). A test bed on which these real-world applications are running in parallel serves serves as a platform for demonstrating and promoting the developed technology. Following are the current application domains:

• In the automotive industry an application was built for customer relationship management, which uses complex text-mining and ontology learning grid-enabled applications.
• In the health sector, we are looking at a grid-enabled and privacy-preserving analysis of geographically widely distributed medical patient records. This use case demonstrates various advantages gained from employing grid technology for this type of task.
• Systems biology is an advanced methodology that is increasingly adopted by the pharmaceutical industry to develop diagnostic, prognostic and therapeutic products and services. This use case explores grid-assisted re-engineering of gene regulatory networks and the analysis of proteins using computational simulations.
• The computer networks use case is concerned with the automated detection of faults and monitoring of complex computer networks. Successful application of distributed data mining technology could help to optimize the operation of computer networks and identify and alleviate costly and mission-critical system failures or performance bottlenecks.
• Civil engineering and ecological modelling has become a critical element in environmental monitoring and conservation. The project is exploring this use case and expects that the developed technology will help to produce more accurate ecological models much faster.
• Text mining is increasingly important in many sectors, including news media, science (e.g., open source publishing), development (e.g., patent repositories), electronic texts (Web, e-mails, digital libraries, etc.), and so on. The project has several use cases dealing with text classification, ontology learning and analysis of digital libraries.

The importance of data mining in many differnet sectors suggests that the project's impact will be significant as it will be an important step towards more effective and efficient exploitation of available data and information resources. In the long run, the impact of the project will contribute to new business and R & D opportunities. By developing a generic Application Description Schema the project also contributes to standardisation efforts of grid and datamining technologies.

Because of its relevance across so many sectors, the DataMiningGrid project has the potential to improve the sharing and exploitation of information.

Updated on June 4, 2008