TY - GEN
T1 - DARE
T2 - 15th IEEE International Conference on eScience, eScience 2019
AU - Klampanos, Iraklis
AU - Davvetas, Athanasios
AU - Gemund, Andre
AU - Atkinson, Malcolm
AU - Koukourikos, Antonis
AU - Filgueira, Rosa
AU - Krause, Amrey
AU - Spinuso, Alessandro
AU - Charalambidis, Angelos
AU - Magnoni, Federica
AU - Casarotti, Emanuele
AU - Page, Christian
AU - Lindner, Mike
AU - Ikonomopoulos, Andreas
AU - Karkaletsis, Vangelis
N1 - Funding Information:
This work has been supported by the EU H2020 research and innovation programme under grant agreement No 777413. 1https://ec.europa.eu/research/openscience/index.cfm?pg= open-science-cloud
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Cloud
KW - Conceptualisation
KW - Data-driven science
KW - Provenance
KW - Scientific workflows
KW - Software platform
KW - Technology
KW - Workflow optimization
UR - http://www.scopus.com/inward/record.url?scp=85083267300&partnerID=8YFLogxK
U2 - 10.1109/eScience.2019.00079
DO - 10.1109/eScience.2019.00079
M3 - Conference contribution
AN - SCOPUS:85083267300
T3 - Proceedings - IEEE 15th International Conference on eScience, eScience 2019
SP - 578
EP - 585
BT - Proceedings - IEEE 15th International Conference on eScience, eScience 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2019 through 27 September 2019
ER -