Ontology-based Design of Experiments on Big Data Solutions

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Zocholl, Maximilian; Camossi, Elena; Jousselme, Anne-Laure; Ray, Cyril;

Big data solutions are designed to cope with data of huge Volume and wide Variety, that need to be ingested at high Velocity and have potential Veracity issues, challenging characteristics that are usually referred to as the "4Vs of Big Data". In order to evaluate possi... View more
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    16 references, page 1 of 2

    which has received funding from the European Unions Horizon 2020 research and innovation programme under

    Grant Agreement No. 687591. [1] ML Schema core specification. Accessed: 2018-02-08. [2] G. Blondet, J. Le Duigou, and N. Boudaoud. Ode: an ontology for numerical design of experiments. Procedia CIRP, 50:496-501, 2016. [3] G. Blondet, J. Le Duigou, N. Boudaoud, and B. Eynard. An ontology for numerical design of experiments processes. Computers in Industry,

    94:26-40, 2018. [4] R. N. Carvalho, R. Haberlin, P. C. G. Costa, K. B. Laskey, and K.-C. Chang. Modeling a probabilistic ontology for maritime domain awareness.

    In Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, pages 1-8. IEEE, 2011. [5] M. Cavazzuti. Optimization methods: from theory to design scientific and technological aspects in mechanics. Springer Science & Business

    Media, 2012. [6] T. Cook and D. Campbell. Quasi-experimentation design and analysis issues for field settings. Rand McNally, 1979. [7] H. Do, S. Elbaum, and G. Rothermel. Supporting controlled experimentation with testing techniques: An infrastructure and its potential impact.

    Empirical Software Engineering, 10(4):405-435, 2005. [8] R. A. Fisher. The design of experiments. Oliver And Boyd; Edinburgh; London, 1937. [9] C. C. Insaurralde and E. Blasch. Veracity metrics for ontological decision-making support in avionics analytics. [10] C. M. Keet, A. Åawrynowicz, C. dAmato, A. Kalousis, P. Nguyen, R. Palma, R. Stevens, and M. Hilario. The data mining optimization

    ontology. Web Semantics: Science, Services and Agents on the World Wide Web, 32:43 - 53, 2015. [11] R. Kitchin and G. McArdle. What makes big data, big data? exploring the ontological characteristics of 26 datasets. Big Data & Society,

    3(1):2053951716631130, 2016. [12] K. Laskey, R. Haberlin, R. Carvalho, and P. Costa. Pr-owl 2 case study: A maritime domain probabilistic ontology. 808:76-83, 11 2011. [13] J. J. Louviere, T. Islam, N. Wasi, D. Street, and L. Burgess. Designing discrete choice experiments: Do optimal designs come at a price?

    Journal of Consumer Research, 35(2):360-375, 2008. [14] D. C. Montgomery. Design and Analysis of Experiments. John wiley & sons, 2017. [15] F. Natale, M. Gibin, A. Alessandrini, M. Vespe, and A. Paulrud. Mapping fishing e ort through ais data. PLOS ONE, 10(6):1-16, 06 2015. [16] P. Panov, L. Soldatova, and S. Dzˇeroski. Ontology of core data mining entities. Data Mining and Knowledge Discovery, 28(5):1222-1265,

    Sep 2014. [17] K. Patroumpas, A. Artikis, N. Katzouris, M. Vodas, Y. Theodoridis, and N. Pelekis. Event recognition for maritime surveillance. In Proceedings

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