The Simple Spectral Access (SSA) Protocol (SSAP) defines a uniform interface to remotely discover and access one-dimensional spectra. SSA is a member of an integrated family of Status of This Document This document has been produced by the IVOA Data Access Layer 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. This document is an update to the earlier SSA V1.04 IVOA Recommendation (February 2008). Changes from the earlier version are summarized in section 9.1. Note from the V1.0 document (this is still planned for a future version): a getCapabilities operation returning service metadata will be added which will eventually obsolete the Many of the ideas in this document originated from others involved in developing Virtual Observatory concepts and standards. In particular, the idea of using association in the query response to group similar datasets grew out of an idea originally proposed by Roy Williams. Arnold Rots originated the idea of ranking query results via a score heuristic, and helped put the coordinate systems used in SSA on firm theoretical foundation via the development of STC. Francois Bonnarel, Mireille Louys, Alberto Micol, and others contributed to the representation of astronomical metadata and in particular the Characterization data model. Laszlo Dobos contributed early implementations of the access protocol using the spectral archive at JHU.
The Data Activated Liu 1 Graph Engine -DALiuGE 2 -is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex data reduction pipelines consisting of both data sets and algorithmic components and an implementation run-time to execute such pipelines on distributed resources. By mapping the logical view of a pipeline to its physical realisation, DALiuGE separates the concerns of multiple stakeholders, allowing them to collectively optimise large-scale data processing solutions in a coherent manner. The execution in DALiuGE is data-activated, where each individual data item autonomously triggers the processing on itself. Such decentralisation also makes the execution framework very scalable and flexible, supporting pipeline sizes ranging from less than ten tasks running on a laptop to tens of millions of concurrent tasks on the second fastest supercomputer in the world. DALiuGE has been used in production for reducing interferometry data sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide Spectral Radioheliograph; and is being developed as the execution framework prototype for the Science Data Processor (SDP) consortium of the Square Kilometre Array (SKA) telescope. This paper presents a technical overview of DALiuGE and discusses case studies from the CHILES and MUSER projects that use DALiuGE to execute production pipelines. In a companion paper, we provide in-depth analysis of DALiuGE's scalability to very large numbers of tasks on two supercomputing facilities.1 Liu is the phonetic form of the Chinese character 流, meaning "flow". 2 Pronounced [da " Ę £lȋW åi] or approximately Da-'Lieu-Gee
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