| Title |
Scaling Big Data Applications in Smart City with Coresets |
| ID_Doc |
35998 |
| Authors |
Trang, LH; Bangui, H; Ge, MZ; Buhnova, B |
| Title |
Scaling Big Data Applications in Smart City with Coresets |
| Year |
2019 |
| Published |
|
| DOI |
10.5220/0007958803570363 |
| 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 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. |
| Author Keywords |
Big Data; Classification; Coreset; Clustering; Sampling; Smart City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000570730200042 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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