Time series data mining for the Gaia variability analysis
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.
Keywords
Cite
@article{arxiv.1411.5943,
title = {Time series data mining for the Gaia variability analysis},
author = {Krzysztof Nienartowicz and Diego Ordóñez Blanco and Leanne Guy and Berry Holl and Isabelle Lecoeur-Taïbi and Nami Mowlavi and Lorenzo Rimoldini and Idoia Ruiz and Maria Süveges and Laurent Eyer},
journal= {arXiv preprint arXiv:1411.5943},
year = {2014}
}
Comments
4 pages, 3 figures. appears in the Proc. of the 2014 conference on Big Data from Space (BiDS14), European Commission, Joint Research Centre, P. Soille, P. G. Marchetti (eds)