Enabling the Definition and Reuse of Multi-Domain Workflow-Based Data Analysis

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 557)


Data analysis applications have become essential to extract significant insight from heterogeneous data sources. However, their development requires technical expertise in computer science techniques like data mining, making its broad adoption by non-experts difficult. In this context, workflows have emerged as a high-level solution to define and automate the sequence of steps involved in the data analysis process, hiding the low-level computational requirements. Existing workflow systems have some difficulties related to their complexity to adapt the provided elements and their inability to reuse workflow definitions. To address these problems, a novel framework for creating customized, ready-to-use and interoperable workflow systems is proposed and prototyped in this paper. Its multi-layer architecture has been designed on the basis of the separation of concerns and the reuse of knowledge assets. As a result, the presented approach allows reducing the time-to-market and saving development costs.


Data analysis Workflow management system Process automation 



Work supported by the Spanish Government, project TIN2014-55252-P.


  1. 1.
    Terminology & glossary. Technical report, WFMC-TC-1011, Workflow Management Coalition (1999)Google Scholar
  2. 2.
    Berthold, M.R., et al.: KNIME: the Konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin (2007)Google Scholar
  3. 3.
    Elmroth, E., Hernández, F., Tordsson, J.: Three fundamental dimensions of scientific workflow interoperability: model of computation, language, and execution environment. Future Gener. Comput. Syst. 26(2), 245–256 (2010)CrossRefGoogle Scholar
  4. 4.
    Fowler, M.: Domain Specific Languages, 1st edn. Addison-Wesley, Boston (2010)Google Scholar
  5. 5.
    Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman & Hall/CRC, Boca Raton (2013)Google Scholar
  6. 6.
    Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: A survey of data-intensive scientific workflow management. J. Grid Comput. 13(4), 457–493 (2015)CrossRefGoogle Scholar
  7. 7.
    Loukides, M.: What is Data Science?. O’Reilly Radar, Sebastopol (2010)Google Scholar
  8. 8.
    Recker, J., Rosa, M.L.: Understanding user differences in open-source workflow management system usage intentions. Inf. Syst. 37(3), 200–212 (2012)CrossRefGoogle Scholar
  9. 9.
    Roure, D.D., Goble, C., Bhagat, J., Cruickshank, D., Goderis, A., Michaelides, D., Newman, D.: myExperiment: defining the social virtual research environment. In: 4th IEEE International Conference on e-Science, pp. 182–189. IEEE Press (2008)Google Scholar
  10. 10.
    Schmidt, D.C.: Guest editor’s introduction: model-driven engineering. Computer 39, 25–31 (2006)CrossRefGoogle Scholar
  11. 11.
    Weske, M., van der Aalst, W., Verbeek, H.: Advances in business process management. Data Knowl. Eng. 50(1), 1–8 (2004)CrossRefGoogle Scholar
  12. 12.
    Wolstencroft, K., Haines, R., Fellows, D., Williams, A., Withers, D., Owen, S., Soiland-Reyes, S., Dunlop, I., Nenadic, A., Fisher, P., Bhagat, J., Belhajjame, K., Bacall, F., Hardisty, A., de la Hidalga, A.N., Balcazar Vargas, M.P., Sufi, S., Goble, C.: The taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Research 41(W1), W557–W561 (2013)CrossRefGoogle Scholar
  13. 13.
    Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3(3), 171–200 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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