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International Journal of Data Science and Analytics

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06.03.2018 | Review

The many faces of data-centric workflow optimization: a survey

verfasst von: Georgia Kougka, Anastasios Gounaris, Alkis Simitsis

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 2/2018

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Abstract

Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a key aspect is data passing through and getting manipulated by a sequence of steps. The large volume and variety of data, the complexity of operations performed, and the long time such workflows take to compute give rise to the need for optimization. In general, data-centric workflow optimization is a technology in evolution. This survey focuses on techniques applicable to workflows comprising arbitrary types of data manipulation steps and semantic inter-dependencies between such steps. Further, it serves a twofold purpose: firstly, to present the main dimensions of the relevant optimization problems and the types of optimizations that occur before flow execution and secondly, to provide a concise overview of the existing approaches with a view to highlighting key observations and areas deserving more attention from the community.

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Hereafter, these three terms will be used interchangeably; the terms workflow and flow will be used interchangeably, too.

The terms technique, proposal, and work will be used interchangeably.

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Titel: The many faces of data-centric workflow optimization: a survey
verfasst von: Georgia Kougka
Anastasios Gounaris
Alkis Simitsis
Publikationsdatum: 06.03.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Data Science and Analytics / Ausgabe 2/2018
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-018-0107-0

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