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The increasing volumes of astronomical data require practical methods for data exploration, access and visualisation. The Hierarchical Progressive Survey (HiPS) is a HEALPix based scheme that enables a multi-resolution approach to astronomy data from the individual pixels up to the whole sky. We highlight the decisions and approaches that have been taken to make this scheme a practical solution for managing large volumes of heterogeneous data. Early implementors of this system have formed a network of HiPS nodes, with some 250 diverse data sets currently available, with multiple mirror implementations for important data sets. This hierarchical approach can be adapted to expose Big Data in different ways. We describe how the ease of implementation, and local customisation of the Aladin Lite embeddable HiPS visualiser have been keys for promoting collaboration on HiPS.
The LSST survey was designed to deliver transformative results for four primary objectives: constraining dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. While the L
Astronomy is entering in a new era of Extreme Intensive Data Computation and we have identified three major issues the new generation of projects have to face: Resource optimization, Heterogeneous Software Ecosystem and Data Transfer. We propose in t
Photometric redshifts (photo-zs) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESO public survey on the VLT Survey T
As current- and next-generation astronomical instruments come online, they will generate an unprecedented deluge of data. Analyzing these data in real time presents unique conceptual and computational challenges, and their long-term storage and archi
This chapter introduces the state-of-the-art in the emerging area of combining High Performance Computing (HPC) with Big Data Analysis. To understand the new area, the chapter first surveys the existing approaches to integrating HPC with Big Data. Ne