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    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    L. Trang, H. Bangui, Mouzhi Ge, B. Buhnova

    Scaling Big Data Applications in Smart City with Coresets

    Proceedings of the 8th International Conference on Data Science, Technology and Applications, vol. Vol. 1: DATA

    2019

    DOI: 10.5220/0007958803570363

    Abstract anzeigen

    With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an ε>0, the method computes an ε-coreset of the dataset. The ε-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.