Scaling Big Data Applications in Smart City with Coresets

Publikace nespadá pod Filozofickou fakultu, ale pod Ústav výpočetní techniky. Oficiální stránka publikace je na webu muni.cz.

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TRANG Le Hong BANGUI Hind GE Mouzhi BÜHNOVÁ Barbora

Druh Článek ve sborníku
Konference Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1
Fakulta / Pracoviště MU

Ústav výpočetní techniky

Citace
www http://dx.doi.org/10.5220/0007958803570363
Doi http://dx.doi.org/10.5220/0007958803570363
Klíčová slova Big Data; Classification; Coreset; Clustering; Sampling; Smart City
Popis 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 e > 0, the method computes an e-coreset of the dataset. The e-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.
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