DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud
| Autor: | Malcolm Atkinson, Emanuele Casarotti, Christian Pagé, Athanasios Davvetas, Andreas Ikonomopoulos, André Gemünd, Rosa Filgueira, Alessandro Spinuso, Federica Magnoni, Mike Lindner, Iraklis A. Klampanos, Angelos Charalambidis, Amrey Krause, Vangelis Karkaletsis, Antonios Koukourikos |
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| Jazyk: | angličtina |
| Předmět: |
Computer science
provenance scientific workflows Cloud computing 02 engineering and technology 010502 geochemistry & geophysics Semantics 01 natural sciences Data-driven Abstraction layer 0202 electrical engineering electronic engineering information engineering workflow optimization cloud conceptualization software platform 0105 earth and related environmental sciences computer.programming_language 020203 distributed computing Conceptualization data-driven science business.industry Python (programming language) Workflow Knowledge base technology business Software engineering computer Agile software development |
| Zdroj: | 2019 15th International Conference on eScience (eScience) UnpayWall Microsoft Academic Graph Edinburgh Research Explorer Klampanos, I, Davvetas, A, Gemünd, A, Atkinson, M, Koukourikos, A, Filgueira Vicente, R, Krause, A, Spinuso, A, Charalambidis, A, Magnoni, F, Casarotti, E, Pagé, C M, Lindner, M, Ikonomopoulos, A & Karkaletsis, V 2020, DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud . in 2019 15th International Conference on eScience (eScience) . Institute of Electrical and Electronics Engineers (IEEE), San Diego, CA, USA, pp. 578-585, Bridging from Concepts to Data and Computation for eScience (BC2DC’19) Workshop, San Diego, California, United States, 24/09/19 . https://doi.org/10.1109/eScience.2019.00079 eScience |
| DOI: | 10.1109/escience.2019.00079 |
| Popis: | The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development. |
| Databáze: | OpenAIRE |
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