| Title |
An empirical study on big video data processing: architectural styles, issues, and challenges |
| ID_Doc |
44522 |
| Authors |
Zhang, WS; Wang, ZC; Xu, L; Zhao, DH; Gong, FM; Lu, QH |
| Title |
An empirical study on big video data processing: architectural styles, issues, and challenges |
| Year |
2016 |
| Published |
|
| DOI |
10.1109/IIKI.2016.7 |
| Abstract |
Video data contributes to the majority of big data, henceforth, how to efficiently and effectively discovering knowledge from large-scale video data becomes a crucial challenge. In this paper, we propose multiple architectural styles for the domain of large-scale video data analytics services. These styles include online combined with offline processing style, distributed shared repositories, image mining and prediction services with deep learning techniques. These architectural styles are successfully implemented and examined in a number of domains including smart traffic and smart drones, as demonstrated in a middleware developed specifically for large-scale continuous video data processing. |
| Author Keywords |
Cloud Computing; big video data processing; architecture styles; Smart City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000426969900022 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
|