D1.2 Architecture Specification

Authors: Christos Doulkeridis, Apostolos Glenis, Giorgos Santipantakis, Akrivi Vlachou, George Vouros, Michael Mock, Nikos Pelekis, Kostas Patroumpas, Elias Alevizos, Georg Fuchs
Title: D1.2 Architecture Specification

This report comprises the second deliverable (D1.2) of datAcron workpackage 1 “System architecture and data management” with main objective to specify the architecture of datAcron, its constituent components, and their interactions, in order to realize the requirements specied in deliverable D1.1.

The first part of this document provides a brief overview of technological solutions that are relevant to the innovative Big Data algorithms and methods that are being developed indatAcron. Its purpose is to narrow down the possible frameworks that can be adopted by datAcron, detect their advantages and disadvantages, and ultimately provide a guide for selecting the most appropriate technological solution for particular parts of the datAcron architecture. The second part of this document describes the proposed datAcron system architecture, the main datAcron components, their interactions, as well as the internals of each component, thereby clarifying the roles and the specific operations performed in datAcron. In addition, a mapping is provided between the requirements identified in deliverable D1.1 and the datAcron architecture proposed in this deliverable, aiming at justifying the choice of the individual components and how the requirements are addressed by the architecture.

The final part of this document presents the current view of the datAcron software architecture, in terms of high level software modules and their interconnections. This part is going to be refined in the following months of the project’s lifetime, and will eventually be documented in deliverables D1.6 and D1.11 about Software Design. However, the usefulness of this part in the present deliverable is to identify novel algorithmic solutions that will be developed in the context of datAcron, as well as contributions in the areas of Big Data processing and analytics.

Year: 2016
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