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Workflow Discovery Through Semantic Constraints: A Geovisualization Case Study

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

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Abstract

The construction of computational pipelines, for example automated cartographic workflows for the construction of thematic maps, typically requires detailed knowledge about the available tools for the individual steps and the technicalities of their composition. It is a time-consuming process and comes with the risk of missing meaningful workflows because many possible pipelines are never taken into account. Automated workflow composition techniques can facilitate comprehensive workflow discovery based on semantic constraints: The users express their intention about the workflows by means of high-level constraints, and receive possible workflows that meet their request. The successful application of such methods essentially depends on the availability and quality of semantic domain models that describe the tools and data types in the domain. In this paper, we present an exemplary domain model for a geovisualization use case, and show how it enables the abstract specification and automated composition of a complex cartographic workflow.

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Change history

  • 27 June 2019

    The original version of the chapter starting on p. 473 unfortunately contained a mistake. The presentation of Figures 4 and 5 was incorrect. The figures have been corrected.

Notes

  1. 1.

    In this context, synthesis refers to the automatic creation of a concrete sequence of semantically described components according to an abstract specification [8].

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, Netherlands

    Vedran Kasalica & Anna-Lena Lamprecht

Authors
  1. Vedran Kasalica
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  2. Anna-Lena Lamprecht
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Corresponding authors

Correspondence to Vedran Kasalica or Anna-Lena Lamprecht .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Kasalica, V., Lamprecht, AL. (2019). Workflow Discovery Through Semantic Constraints: A Geovisualization Case Study. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_34

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  • DOI: https://doi.org/10.1007/978-3-030-24302-9_34

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