Addressing Big Data challenges for Scientific Data Infrastructure

This paper discusses the challenges that are imposed by Big Data Science on the modern and future Scientific Data Infrastructure (SDI). The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific Data Lifecycle Management (SDLM) model that includes all the major stages and reflects specifics in data management in modern e-Science. The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices. The paper explains how the proposed models SDLM and SDI can be naturally implemented using modern cloud based infrastructure services provisioning model.

I. Introduction

Modern e-Science infrastructure allows targeting new large scale problems which solution was not possible before, e.g. genome, climate, global warming, etc. e-Science typically produces a huge amount of data that need to be supported by a new type of e-Infrastructure capable to store, distribute, process, preserve, and curate these data: we refer to this new infrastructures as Scientific Data e-Infrastructure (SDI) [1], [2].