TY - GEN
T1 - Harnessing Big Data and Analytics Solutions in Support of Smart City Services
AU - Pandey, Shailesh Kumar
AU - Khan, Mohammad Tariq
AU - Qiu, Robin G.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126202095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126202095&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04726-9_12
DO - 10.1007/978-3-030-04726-9_12
M3 - Conference contribution
AN - SCOPUS:85126202095
SN - 9783030047252
T3 - Springer Proceedings in Business and Economics
SP - 117
EP - 128
BT - Advances in Service Science - Proceedings of the 2018 INFORMS International Conference on Service Science
A2 - Yang, Hui
A2 - Qiu, Robin
PB - Springer Science and Business Media B.V.
T2 - INFORMS International Conference on Service Science, ICSS 2018
Y2 - 3 November 2018 through 3 November 2018
ER -