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Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements

Objectif

The rapid increase in demand for data-intensive applications capable of exploiting Big Data technologies such as Hadoop/MapReduce, NoSQL, cloud-based storage, and stream processing is creating massive growth opportunities for European independent software vendors (ISVs). However, developing software that meets the high-quality standards expected for business-critical cloud applications remains a barrier to this market for many small and medium ISVs, which often lack resources and expertise for advanced quality engineering.

DICE will tackle this challenge by defining a quality-driven development methodology and related tools that will markedly accelerate the development of business-critical data-intensive applications running on public or private clouds. Building on the principles of model-driven development (MDD) and on popular standards such as UML, MARTE and TOSCA, the project will first define a novel MDD methodology that can describe data and data-intensive technologies in cloud applications. A quality engineering toolchain offering simulation, verification, and numerical optimisation will leverage these extensions to drive the early design stages of the application development and guide software quality evolution.

DevOps-inspired methods for deployment, testing, continuous integration and monitoring feedback analysis will be used to accelerate the incorporation of quality in data-intensive cloud application both in public and private deployments, enhancing the capability of small and medium European ISVs to enter the Big Data market.

Appel à propositions

H2020-ICT-2014

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Sous appel

H2020-ICT-2014-1

Coordinateur

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Contribution nette de l'UE
€ 712 059,00
Adresse
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ LONDON
Royaume-Uni

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Région
London Inner London — West Westminster
Type d’activité
Higher or Secondary Education Establishments
Liens
Coût total
€ 712 059,00

Participants (9)