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Register a new userTarget and (Astro-)WISE technologies - Data federations and its applications
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After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS, the MONK handwritten text recognition system, and business applications, by amongst others, the Target Holding. We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.
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In this paper we describe the way the Astro-WISE information system (or simply Astro-WISE) supports the data from a wide range of in- struments and combines multiple surveys and their catalogues. Astro-WISE allows ingesting of data from any optical instrument, survey or catalogue, pro- cessing of this data to create new catalogues and bringing in data from different surveys into a single catalogue, keeping all dependencies back to the original data. Full data lineage is kept on each step of compiling a new catalogue with an ability to add a new data source recursively. With these features, Astro- WISE allows not only combining and retrieving data from multiple surveys, but performing scientific data reduction and data mining down to the rawest data in the data processing chain within a single environment.
The Target infrastructure has been specially built as a storage and compute infrastructure for the information systems derived from Astro-WISE. This infrastructure will be used by several applications that collaborate in the area of information systems within the Target project. It currently consists of 10 PB of storage and thousands of computational cores. The infrastructure has been constructed based on the requirements of the applications. The storage is controlled by the Global Parallel File System of IBM. This file system takes care of the required flexibility by combining storage hardware with different characteristics into a single file system. It is also very scalable, which allows the system to be extended into the future, while replacing old hardware with new technology.
The Kilo Degree Survey (KiDS) is a 1500 square degree optical imaging survey with the recently commissioned OmegaCAM wide-field imager on the VLT Survey Telescope (VST). A suite of data products will be delivered to ESO and the community by the KiDS survey team. Spread over Europe, the KiDS team uses Astro-WISE to collaborate efficiently and pool hardware resources. In Astro-WISE the team shares, calibrates and archives all survey data. The data-centric architectural design realizes a dynamic live archive in which new KiDS survey products of improved quality can be shared with the team and eventually the full astronomical community in a flexible and controllable manner
The OmegaCAM wide-field optical imager is the sole instrument on the VLT Survey Telescope at ESOs Paranal Observatory. The instrument, as well as the telescope, have been designed for surveys with very good, natural seeing-limited image quality over a 1 square degree field. OmegaCAM was commissioned in 2011 and has been observing three ESO Public Surveys in parallel since October 15, 2011. We use the Astro-WISE information system to monitor the calibration of the observatory and to produce the Kilo Degree Survey (KiDS). Here we describe the photometric monitoring procedures in Astro-WISE and give a first impression of OmegaCAMs photometric behavior as a function of time. The long-term monitoring of the observatory goes hand in hand with the KiDS survey production in Astro-WISE. KiDS is observed under partially non-photometric conditions. Based on the first year of OmegaCAM operations it is expected that a $sim 1%-2%$ photometric homogeneity will be achieved for KiDS.
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streaming, VR is with ultra high-definition and dynamically changes with users head and eye movements, which poses significant challenges for the realization of such potential. In this paper, we provide a detailed and systematic survey of enabling technologies of virtual reality and its applications in Internet of Things (IoT). We identify major challenges of virtual reality on system design, view prediction, computation, streaming, and quality of experience evaluation. We discuss each of them by extensively surveying and reviewing related papers in the recent years. We also introduce several use cases of VR for IoT. Last, issues and future research directions are also identified and discussed.
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