IVOA Knowledge Discovery in Databases

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Charter

We will develop and test scalable data mining algorithms and the accompanying new standards for VO interfaces and protocols, so that these algorithms can be discovered and used transparently within VO science workflows or in standalone data exploration applications. Therefore the activities of the KDD-IG will be:

1. Support the definition of an ontology of the KDD tasks required by the astronomical community. This ontology will be used to define programming and documentation standards.
2. Make an inventory of existing methods relevant for astrophysical applications (more than 100 new KDD models and methods appear every month on specialized journals).
3. Identify reference data sets to be used for comparing, debugging and testing methods and tools.
4. Foster the implementation, using available VO standards and methods, of general purpose data exploration and data mining methods which will allow the general userto seamlessly exploit the complex data repositories offered by the VO.
5. Provide/receive feedbacks to/from the WGs in order to improve the usability of VO tools and standards.
6. Provide/receive from the community information to improve both the usability and the potentialities of Data Mining tools under the VO.
7. Define and pursue specific science cases which will be used to showcase the VO capabilities to the community.

More important than anything else, we wish to use this IG as an arena where different groups can share experiences and plan future developments.

KDD Group Meetings

Next tasks

1. Definition of a taxonomy of Data Mining models. This taxonomy will contribute to the Standard Vocabulary of the Semantics WG2.
2. Definition of the requirements which a Data Mining model needs to match in order to be imported under the VObs standards.
3. Inventory of existing Data Mining models of relevant astrophysical interest. 1.4 Definition of standard template data sets for Data Mining models test and debugging.
4. Definition of standard data sets to be used as bases of knowledge for debugging and test of supervised methods.
5. Definition of procedures to extract and validate robust bases of knowledge from the VObs data archives using the VObs ontology.
6. Study of the scalability of Data Mining models under different computing infrastructures (definition of best benchmarks).

Members

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