IVOA Architecture Version 1.0

Arviset, C.; Gaudet, S.

doi:10.5479/ads/bib/2010ivoa.rept.1123a

Cited by 19 publications

(13 citation statements)

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“…An additional peculiarity is that these large and heterogeneous data sets [7] need to be simultaneously queued, merged and mined by many independent groups of researchers, posing problems not common in many other application domains. In this context it is important to mention the crucial role played by the International Virtual Observatory Alliance -IVOA [8] which aims at a seamless access to astronomical data and focuses on the standardization of data and metadata, data exchange methods, and to the implementation of a registry, which lists available services and what can be done with them.…”

Section: The Need For Machine Learning In Astronomymentioning

confidence: 99%

Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics

Longo¹,

Merényi

Tiňo

2019

PASP

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Astronomical observations already produce vast amounts of data through a new generation of telescopes (Atacama Large Millimeter Array (ALMA), Jansky VLA) and through large surveys (e.g., SDSS [1], ZTF [2], PanSTARRS [3], VLT Survey Telescope -VST, and many others) that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope (LSST [4]) and the Square Kilometer Array (SKA [5]) are planned to become operational in this decade and the next, and will increase the data volume by many orders of magnitude. The increased spatial, temporal and spectral resolution afford a powerful magnifying lens on the physical processes that underlie the data but, at the same time, generate unprecedented complexity hard to exploit for knowledge extraction. It is therefore imperative to develop machine intelligence, machine learning (ML) in particular, suitable for processing the amount and variety of astronomical data that will be collected, and capable of answering scientific questions based on the data [6].Astronomical data exhibit the usual challenges associated with big data such as immense volumes, high dimensionality, missing or highly distorted observations. In addition, astronomical data can exhibit large continuous observational gaps, very low signal-to-noise ratio and the need to distinguish between true missing (i.e., noncollected) data and non-detections (i.e., due to upper limits). There are strict laws of physics behind the data production which can be assimilated into ML mechanisms to improve over general off-the-shelf state-of-the-art methods. An additional peculiarity is that these large and heterogeneous data sets [7] need to be simultaneously queued, merged and mined by many independent groups of researchers, posing problems not common in many other application domains. In this context it is important to mention

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Section: The Need For Machine Learning In Astronomymentioning

confidence: 99%

Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics

Longo¹,

Merényi

Tiňo

2019

PASP

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“…A lot of the most recent activity in this field has centred on Docker and this paper presents lessons learned from two projects we have conducted involving Docker: a simple test of its capabilities as a deployment system and as part of more complicated project connecting a range of Virtual Observatory (VO; Arviset et al 2010) services running in separate Docker containers.…”

Section: Introductionmentioning

confidence: 99%

Use of Docker for deployment and testing of astronomy software

Morris

Voutsinas

Hambly

et al. 2017

Astronomy and Computing

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We describe preliminary investigations of using Docker for the deployment and testing of astronomy software. Docker is a relatively new containerisation technology that is developing rapidly and being adopted across a range of domains. It is based upon virtualization at operating system level, which presents many advantages in comparison to the more traditional hardware virtualization that underpins most cloud computing infrastructure today. A particular strength of Docker is its simple format for describing and managing software containers, which has benefits for software developers, system administrators and end users.We report on our experiences from two projects -a simple activity to demonstrate how Docker works, and a more elaborate set of services that demonstrates more of its capabilities and what they can achieve within an astronomical context -and include an account of how we solved problems through interaction with Docker's very active open source development community, which is currently the key to the most effective use of this rapidly-changing technology.

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“…The SAMP standard itself is neither a dependent, nor a dependency, of other VO standards, but it provides valuable glue between user-level applications which perform different VO-related tasks, and hence contributes to the integration of Virtual Observatory functionality from a user's point of view. Figure 1 illustrates SAMP in the context of the IVOA Architecture [1]. Most existing tools which operate in the User Layer of this architecture provide SAMP interoperability.…”

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confidence: 99%

“…Most existing tools which operate in the User Layer of this architecture provide SAMP interoperability. [1]. The SAMP protocol appears in the "Using" region.…”

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confidence: 99%

Simple Application Messaging Protocol Version 1.3

Taylor¹,

Boch²,

Fitzpatrick³

et al.

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SAMP is a messaging protocol that enables astronomy software tools to interoperate and communicate. IVOA members have recognised that building a monolithic tool that attempts to fulfil all the requirements of all users is impractical, and it is a better use of our limited resources to enable individual tools to work together better. One element of this is defining common file formats for the exchange of data between different applications. Another important component is a messaging system that enables the applications to share data and take advantage of each other's functionality. SAMP supports communication between applications on the desktop and in web browsers, and is also intended to form a framework for more general messaging requirements. Status of this Document This document has been produced by the IVOA Applications Working Group. It has been reviewed by IVOA Members and other interested parties, and has been endorsed by the IVOA Executive Committee as an IVOA Recommendation. It is a stable document and may be used as reference material or cited as a normative reference from another document. IVOA's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability inside the Astronomical Community. Comments, questions and discussions relating to this document may be posted to the mailing list of the SAMP subgroup of the Applications Working Group,

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IVOA Architecture Version 1.0

Cited by 19 publications

References 0 publications

Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics

Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics

Use of Docker for deployment and testing of astronomy software

Simple Application Messaging Protocol Version 1.3

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