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arXiv · 1411.5943

Time series data mining for the Gaia variability analysis

Krzysztof Nienartowicz·Diego Ordóñez Blanco·Leanne Guy·Berry Holl·Isabelle Lecoeur-Taïbi·Nami Mowlavi·Lorenzo Rimoldini·Idoia Ruiz·Maria Süveges·Laurent Eyer
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Abstract

Gaia is an ESA cornerstone mission, which was successfully launched December 2013 and commenced operations in July 2014. Within the Gaia Data Processing and Analysis consortium, Coordination Unit 7 (CU7) is responsible for the variability analysis of over a billion celestial sources and nearly 4 billion associated time series (photometric, spectrophotometric, and spectroscopic), encoding information in over 800 billion observations during the 5 years of the mission, resulting in a petabyte scale analytical problem. In this article, we briefly describe the solutions we developed to address the challenges of time series variability analysis: from the structure for a distributed data-oriented scientific collaboration to architectural choices and specific components used. Our approach is based on Open Source components with a distributed, partitioned database as the core to handle incrementally: ingestion, distributed processing, analysis, results and export in a constrained time window.

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Krzysztof Nienartowicz, Diego Ordóñez Blanco, Leanne Guy, Berry Holl, Isabelle Lecoeur-Taïbi, Nami Mowlavi, Lorenzo Rimoldini, Idoia Ruiz, Maria Süveges, Laurent Eyer. 2014-11-21. Time series data mining for the Gaia variability analysis. https://doi.org/10.2788/1823

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