| Title |
Harnessing Big Data and Analytics Solutions in Support of Smart City Services |
| ID_Doc |
38201 |
| Authors |
Pandey, SK; Khan, MT; Qiu, RG |
| Title |
Harnessing Big Data and Analytics Solutions in Support of Smart City Services |
| Year |
2019 |
| Published |
|
| DOI |
10.1007/978-3-030-04726-9_12 |
| Abstract |
Connecting and leveraging different types of electronic data sources (e.g., mobile and networked sensors, devices, and systems) to create an integrated platform is always a challenging task. To meet the needs of smart city development, developing that platform to process collected data in real time to support smart city services becomes essential. A robust and scalable framework for integrating big data and analytics solutions thus is required, aimed at providing seamless integration of heterogeneous data to manage city transportation, traffic, energy consumption, schools, hospitals, and other public services in a smart and sustainable manner. This paper extends our preliminary framework studies by discussing how we can implement physical and social sensing using the proposed big data and analytics platform to enable better and smarter services than ever before in great detail. With the support of big data and analytics technologies, we use city mobility services to demonstrate the great potential of the proposed integration and aggregation framework. Specifically, real time data from Citi Bike is collected, processed, and modeled. The developed prototype in support of city mobility management and operations shows a variety of potential benefits of the proposed digital ecosystem platform. |
| 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); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) |
| EID |
WOS:000476922800012 |
| WoS Category |
Business; Engineering, Industrial; Management; Operations Research & Management Science |
| Research Area |
Business & Economics; Engineering; Operations Research & Management Science |
| PDF |
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