Mining Knowledge in Astrophysical Massive Data Sets

Details Mining Knowledge in Astrophysical Massive Data Sets Mining Knowledge in Astrophysical Massive Data Sets

2010-10-19

arxiv.org/abs/1010.3796v1

Elsevier, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

Pages 845-849 1rs International Conference on Frontiers in Diagnostics Technologies

Scientific paper

10.1016/j.nima.2010.02.002

Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists into the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such effort being that once the infrastructure will be completed, it will allow a new type of multi-wavelength, multi-epoch science which can only be barely imagined. Data Mining, or Knowledge Discovery in Databases, while being the main methodology to extract the scientific information contained in such MDS (Massive Data Sets), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced Data Mining methodologies in the case of the DAME (DAta Mining & Exploration) project.

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