A computational theoretical approach for mining data on transient events from databases of high energy astrophysics experiments. (arXiv:2004.04131v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Lazzarotto_F/0/1/0/all/0/1">Francesco Lazzarotto</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Feroci_M/0/1/0/all/0/1">Marco Feroci</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pazienza_M/0/1/0/all/0/1">Maria Teresa Pazienza</a>
Data on transient events, like GRBs, are often contained in large databases
of unstructured data from space experiments, merged with potentially large
amount of background or simply undesired information. We present a
computational formal model to apply techniques of modern computer science -such
as Data Mining (DM) and Knowledge Discovering in Databases (KDD)- to a generic,
large database derived from a high energy astrophysics experiment. This method
is aimed to search, identify and extract expected information, and maybe to
discover unexpected information .
Data on transient events, like GRBs, are often contained in large databases
of unstructured data from space experiments, merged with potentially large
amount of background or simply undesired information. We present a
computational formal model to apply techniques of modern computer science -such
as Data Mining (DM) and Knowledge Discovering in Databases (KDD)- to a generic,
large database derived from a high energy astrophysics experiment. This method
is aimed to search, identify and extract expected information, and maybe to
discover unexpected information .
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