Authors:
Le Hong Trang
1
;
Hind Bangui
2
;
Mouzhi Ge
2
and
Barbora Buhnova
2
Affiliations:
1
Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam National University, Ho Chi Minh City and Vietnam
;
2
Institute of Computer Science, Masaryk University, Brno, Czech Republic, Faculty of Informatics, Masaryk University, Brno and Czech Republic
Keyword(s):
Big Data, Classification, Coreset, Clustering, Sampling, Smart City.
Abstract:
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.