| Title |
Event Detection for Smarter Cities |
| ID_Doc |
43376 |
| Authors |
Borges, J; Hain, H; Sudrich, S; Beigl, M |
| Title |
Event Detection for Smarter Cities |
| Year |
2017 |
| Published |
|
| DOI |
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| Abstract |
Detecting what is going on around in near real time through analysis of social network data has become part of assessing the pulse of a city. The advances in event detection techniques enable cities to give a real-time overview of the events -ranging from music concerts and exhibitions to emergencies like fires and car accidents - and activities happening in a smart city. While previous work mostly focused on large-scale, e.g. global or national level, event detection recent development focuses on hyper-local events, that are occurring in a small region, e.g. a street corner or a certain venue rather than city-or country-level area. This paper offers a broad survey and classification of event detection techniques, identifying the key features of recent techniques and their usage in the context of smart cities. This is done by introducing them as well as comparing and categorizing different up to date techniques regarding their event definition, their mode of operation, and their qualitative and quantitative evaluation approaches. Research gaps are highlighted and future work in the area is identified. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000464418300198 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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