A computational theoretical approach for mining data on transient events from databases of high energy astrophysics experiments
This work addresses the challenge of data extraction for researchers in high-energy astrophysics, but it appears incremental as it applies existing computer science methods to a specific domain without claiming major breakthroughs.
The authors tackled the problem of extracting transient event data from large, unstructured astrophysics databases by developing a computational formal model that applies data mining and knowledge discovery techniques, aiming to identify both expected and 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 .