Supporting Data-Intensive Analysis Processes: A Review of Enabling Technologies and Trends

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Author(s): Lawrence Yao (University of New South Wales, Australia), Fethi A. Rabhi (University of New South Wales, Australia)and Maurice Peat (University of Sydney, Australia)
Copyright: 2014
Pages: 28
Source title: Handbook of Research on Architectural Trends in Service-Driven Computing
Source Author(s)/Editor(s): Raja Ramanathan (Independent Researcher, USA)and Kirtana Raja (IBM, USA)
DOI: 10.4018/978-1-4666-6178-3.ch019

Keywords: Computer Science & IT / Information Science Reference / Systems & Software Design / Systems and Software Engineering

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Abstract

Research scientists in data-intensive science use a variety of scientific software applications to support their analyses and processes. To efficiently support the work of these scientists, software applications should satisfy the essential requirements of interoperability, integration, automation, reproducibility, and efficient data handling. Various enabling technologies including workflow, service, and portal can be used to address these essential requirements. Through an in-depth review, this chapter illustrates that no one technology can address all of the essential requirements of scientific processes and therefore necessitates the use of hybrid technologies to support the requirements of data-intensive research. The chapter also describes current scientific applications that utilize a combination of technologies and discusses some future research directions.

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Supporting Data-Intensive Analysis Processes: A Review of Enabling Technologies and Trends

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