Zusammenfassung
Modern scientific data mainly consist of huge data
sets gathered by a very large number of techniques and
stored in much 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 of 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 an effort is
that once the infrastructure is complete, 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 Massive Data Sets (MDS), 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 and Exploration)
project.
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