Abstract
The Gaia mission (ESA) is collecting huge amounts of information about the objects that populate our Galaxy and beyond. Such data must be processed and analyzed before being released, and this work is carried out by the Data Processing and Analysis Consortium (DPAC) through several work packages. One of these packages is Outlier Analysis, devoted to the study, by means of unsupervised clustering, of all the objects that cannot be fitted into any of the existent models. An algorithm based on optimized Self-Organized Maps (SOM) is proposed and implemented for taking advantage of distributed computing platforms, such as the MapReduce paradigm for Apache Hadoop and Apache Spark. Finally, the processing times of the sequential implementation of the algorithm is compared to the Hadoop and Spark based ones.
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