Parallel and Distributed Data Mining for Astronomy Applications

Das, Kunal; Bhaduri, Kanishka

doi:10.1201/b11822-35

Advances in Machine Learning and Data Mining for Astronomy

2012

DOI: 10.1201/b11822-35

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Parallel and Distributed Data Mining for Astronomy Applications

Kunal Das¹,

Kanishka Bhaduri²

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2014

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“…Creating implementations targeted for High-Performance Computing (HPC) architectures. Traditional analysis methods used in astronomy do not scale to peta-scale volumes ofdata on a single computer[57]. One could rework the algorithms to improve computational efficiency but even this might prove to be insufficient with the new surveys.…”

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confidence: 99%

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Huijse

Estévez

Protopapas

et al. 2014

IEEE Comput. Intell. Mag.

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Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky.The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.

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mentioning

confidence: 99%

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Huijse

Estévez

Protopapas

et al. 2014

IEEE Comput. Intell. Mag.

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Parallel and Distributed Data Mining for Astronomy Applications

Cited by 1 publication

References 40 publications

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

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