Title:	DRIVING MODEL OF BIG DATA GOVERNANCE IN URBAN COMMUNITY SAFETY SERVICE
Author(s):	Zhao-Ge Liu and Xiang-Yang Li
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Community, Public Safety, Safety Service, Big Data Governance, Driving Model
Type:	Full Paper
First Page:	47
Last Page:	53
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Community is the basic unit of public safety management. A good community safety service is the key of improving community safety level. At the era of data explosion, big data based decision making has been widely adopted to facilitate community safety service. To achieve big data sharing and service value increasing, big data governance is the prime mode. Current big data governance studies lack an integrated model for comprehensively solving different data sharing problems. Models have dual advantages including supporting organizational synergy and supporting resource reuse. This paper puts forward a kind of driving model of big data governance in urban community safety service, integrating scenario-based driven, data-based driven and model-based driven approaches. The scenario-driven part of driving model provides the overall solutions for big data governance. The data-based driven level provides big data governance with objective evidence. The model-based level provides effective operational modes to the problem solving in big data governance. Through a use case, the rationality and effectiveness of the driving model are verified.
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