Astronomy is at the forefront of Big Data science, with exponentially growing data volumes and rates, and an ever-increasing complexity, now entering the Petascale regime. Forthcoming and future generations of instruments and related survey projects (SKA, LSST, EUCLID, E-ELT, GAIA, JWST, Athena or CTA to quote just a few), from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are and will be connected through the Virtual Observatory framework that provides interoperability standards and services, and effectively constitutes a global “Big Data” grid of astronomy. Furthermore, numerical simulations (requiring HPC/HTC infrastructures and advanced data modeling and analytics) are no longer just a crutch of theory, but are increasingly becoming the dominant or even the only way in which various complex phenomena can be modeled, explored and understood. Making discoveries in this overabundance of data requires however novel data-driven and data-science solutions.
The proposed Workshop has two main goals. First, to give a theoretical and pragmatic view of the state-of-the-art within newborn disciplines like data science and Astroinformatics. The perspective would be to drive the italian astrophysical community through the large variety of tools and applications potentially able to help modern scientists in the investigation on large and heterogeneous data volumes. Second, to focus on the analysis and discussions about the problems of balancing the incoming data deluge with well-proportioned data mining and machine (deep) learning solutions, based on robust and efficient processing/simulation environments and scalable data analytics methods, including Cloud Computing and the Machine Learning As a Service (MLaaS) approach.