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SUMMARY
The shift towards e-Science and the use of computational techniques in the sciences brings with it the need for effective analyses of very large collections of often complex scientific data.
Much of today’s data handling, ranging from measuring relevant variables, to data collection, to integration, to analysis, and finally to decision making, is processed through automated techniques. The term e-science (electronic or enhanced science) is used to denote this type of data intensive and computationally intensive work in collaborative science. Analytical results and decision making are based on various processes, each of which might introduce errors.
This project aims to provide a foundation for e-Science by developing novel techniques that enable scientists to detect and correct anomalies in source data, in an on-line, interactive, lineage-preserving, and semi-automatic manner. This contrasts traditional algorithms that operate in a batch manner on static data and require data mining expertise for their use. We propose a new paradigm that allows domain experts to tap into the full potential of data mining by inventing scalable algorithms that build on insights from most notably the area of subspace clustering to offer effective foundations for anomaly detection and correction that render subsequent analyses robust in the context of imperfect source data.
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SOLUTIONS
- Identifying relevant subspace projections in high dimensional data
- Scaling to large scale data collections
- Integrating seamlessly into the eScience lifecycle
- Handling dynamic data incrementally and efficiently
- Making use0friendly for those with little or no data mining expertise
- Allowing for feedback and updating by domain experts
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LINK: http://itu.dk/en/Forskning/Forskningsprojekter/SmartOSN
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