DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

Iraklis Klampanos, Athanasios Davvetas, André Gemünd, Malcolm Atkinson, Antonis Koukourikos, Rosa Filgueira Vicente, Amrey Krause, Alessandro Spinuso, Angelos Charalambidis, Federica Magnoni, Emanuele Casarotti, Christian M. Pagé, Mike Lindner, Andreas Ikonomopoulos, Vangelis Karkaletsis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 API, which are suitable for raising the abstraction level and hiding complexity. It 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.
Original languageEnglish
Title of host publication2019 15th International Conference on eScience (eScience)
Place of PublicationSan Diego, CA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-7281-2451-3
ISBN (Print)978-1-7281-2452-0
Publication statusPublished - 19 Mar 2020
EventBridging from Concepts to Data and Computation for eScience (BC2DC’19) Workshop: A workshop co-located with the eScience 2019 International Conference - San Diego, California, United States
Duration: 24 Sep 201924 Sep 2019


WorkshopBridging from Concepts to Data and Computation for eScience (BC2DC’19) Workshop
Abbreviated titleBC2DC 2019
Country/TerritoryUnited States
CitySan Diego, California
Internet address


  • software platform
  • cloud
  • technology
  • conceptualization
  • data-driven science
  • scientific workflows
  • provenance
  • workflow optimization


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