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

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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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Notes

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

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

  3. Through considering optimizations starting from a valid initial flow, we exclude from our survey the big area of answering queries in the presence of limited access patterns, in which, the main aim is to construct such an initial plan [69, 78] through selecting an appropriate subset of tasks from a given task pool; however, we have considered works from data integration that optimize the plan after it has been devised, such as [111] or [34], which is subsumed by Kougka and Gounaris [60].

  4. www.myexperiment.org/ in bio-informatics.

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  1. Aristotle University of Thessaloniki, Thessaloníki, Greece

    Georgia Kougka & Anastasios Gounaris

  2. HP Labs, Palo Alto, USA

    Alkis Simitsis

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Kougka, G., Gounaris, A. & Simitsis, A. The many faces of data-centric workflow optimization: a survey. Int J Data Sci Anal 6, 81–107 (2018). https://doi.org/10.1007/s41060-018-0107-0

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