Engineering Data Intensive Applications with Cadral

  • Yoann Didry
  • Olivier Parisot
  • Thomas Tamisier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9320)


Developed through many industrial and research partnerships, the software platform Cadral addresses operational needs of organizations by integrating two complementary modules: a collaborative decision support framework and a visual analytics tool suite for knowledge extraction and data processing. It is used to support the designing of innovative applications, facilitates the comparison and selection of up-to-date technologies and the release of specific pieces of software for operational purposes.


Visual analytics Knowledge extraction Decision support 


  1. 1.
    Sui, L.: Decision support systems based on knowledge management. In: International Conference on Services Systems and Services Management (ICSSSM) (2005)Google Scholar
  2. 2.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)Google Scholar
  3. 3.
    Law, M.H.C., Jain, A.K.: Incremental nonlinear dimensionality reduction by manifold learning. IEEE Trans. Pattern Analysis Mach. Intell. 28, 377–391 (2006)CrossRefGoogle Scholar
  4. 4.
    Bruneau, P., et al.: Cluster sculptor, an interactive visual clustering system. Neurocomputing 150, 627–644 (2015)CrossRefGoogle Scholar
  5. 5.
    Parisot, O., et al.: Using clustering to improve decision trees visualization. In: 17th International Conference on Information Visualisation (IV), pp. 186–191 (2013)Google Scholar
  6. 6.
    Tamisier, T., Parisot, O., Didry, Y., Wax, J., Feltz, F.: Adapting decision support to business requirements through data interpretation. In: Luo, Y. (ed.) CDVE 2011. LNCS, vol. 6874, pp. 82–85. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Bruneau, P., et al.: Finding relevant features for statistical speech synthesis adaptation. In: The 9th Language Resources and Evaluation Conference (LREC), VisLR 2014 Workshop (2014)Google Scholar
  8. 8.
    Parisot, O., et al.: Visual analytics for supporting manufacturers and distributors in online sales. In: 6th International Workshop on Enterprise Modelling and Information Systems Architectures (EMISA 2014) (2014)Google Scholar
  9. 9.
    Giustarini, L., et al.: Data-infilling in daily mean river flow records: first results using a visual analytics tool (gapIt). In: European Geosciences Union General Assembly 2015 (EGU 2015), vol. 17 (2015)Google Scholar
  10. 10.
    Boudjeloud Assala, L., et al.: Interactive and iterative visual clustering. Inf. Vis. 1–7 (2015)Google Scholar
  11. 11.
    Serban, F., et al.: A survey of intelligent assistants for data analysis. ACM Comput. Surv. (CSUR) 45(3), 31 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Luxembourg Institute of Science and Technology (LIST)BelvauxLuxembourg

Personalised recommendations