Encyclopedia of Education and Information Technologies

2020 Edition
| Editors: Arthur Tatnall

Modeling of Scientific Workflows

  • Anna-Lena LamprechtEmail author
  • Tiziana MargariaEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-030-10576-1_210
  • 4 Downloads

Introduction

In the age of big data, scientists of all disciplines need to become data scientists to analyze increasing amounts of research data. Often the available standard software is not sufficient for specific research purposes, so that the development of own, purpose-specific software becomes necessary. Hence, computational thinking, programming, and software development skills are not only taught in computer science and closely related study programs anymore but increasingly to students of all disciplines (Marshall 2017).

Many of the software applications that (data) scientists develop for their work are however not written from scratch. Rather, they reuse existing computational components such as programming libraries, web services, and command-line tools and combine them anew for the specific purpose. The applications hence “orchestrate” components, by defining the execution order of the components and the flow of data between them. Over the last years, the scientific...

This is a preview of subscription content, log in to check access.

References

  1. Callahan SP, Freire J, Santos E, Scheidegger CE, Silva CT, Vo HT (2006) Vistrails: visualization meets data management. In: Proceedings of the 2006 ACM SIGMOD international conference on management of data, SIGMOD ’06. ACM, New York, pp 745–747CrossRefGoogle Scholar
  2. Gil Y (2014) Teaching parallelism without programming: a data science curriculum for non-cs students. In: 2014 workshop on education for high performance computing, pp 42–48. https://ieeexplore.ieee.org/document/7016357
  3. Gil Y (2016) Teaching big data analytics skills with intelligent workflow systems. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, AAAI’16. AAAI Press, Palo Alto, pp 4081–4088Google Scholar
  4. Gil Y, Ratnakar V, Kim J, Gonzalez-Calero P, Groth P, Moody J, Deelman E (2011) Wings: intelligent workflow-based design of computational experiments. IEEE Intell Syst 26(1):62–72CrossRefGoogle Scholar
  5. Lamprecht A-L, Margaria T (eds) (2014) Process design for natural scientists – an agile model-driven approach. Communications in computer and information science, vol 500. Springer, Berlin/HeidelbergGoogle Scholar
  6. Lamprecht A-L, Margaria T (2015) Scientific workflows with XMDD: a way to use process modeling in computational science education. Procedia Comput Sci 51:1927–1936. International Conference On Computational Science, ICCS 2015CrossRefGoogle Scholar
  7. Lamprecht A, Steffen B, Margaria T (2016) Scientific workflows with the jABC framework – a review after a decade in the field. Int J Softw Tools Technol Transfer 18:1–23. https://link.springer.com/article/10.1007/s10009-016-0427-0
  8. Marshall B (2017) Data science experiences for undergraduates. J Comput Sci Coll 33(2):198–204Google Scholar
  9. Silva CT, Anderson E, Santos E, Freire J (2011) Using VisTrails and provenance for teaching scientific visualization. Comput Graphics Forum 30(1):75–84CrossRefGoogle Scholar
  10. Steffen B, Margaria T, Nagel R, Jörges S, Kubczak C (2007) Model-driven development with the jABC. In: Bin E, Ziv A, Ur S (eds) Hardware and software, verification and testing, Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 92–108CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information and Computing SciencesUtrecht UniversityUtrechtNetherlands
  2. 2.Department of Computer Science and Information SystemsUniversity of LimerickLimerickIreland
  3. 3.Lero – the Irish Software Research CentreLimerickIreland
  4. 4.Confirm – Centre for Smart ManufacturingLimerickIreland

Section editors and affiliations

  • Bill Davey
    • 1
  1. 1.RMIT UniversityMelbourneAustralia