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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(1): 44-50 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.481

Skyguard: A Context-Aware Mobile Information System for Emergency Management

Nan Jing
Abstract—Context awareness is one of the most critical components in building information systems because the systems use that to warrant the relevance of provided information and users’ needs. Existing research work in context awareness mainly focused on analyzing either Internetbased web pages or user search keywords. There are works that develop and utilize context awareness in the domains of information system and emergency management, however these works have not specified a comprehensive framework and a clear workflow about how exactly the critical contexts are captured and utilized to produce more effective and relevant instructions. In this paper, we have reviewed the related works in the cross domains of context awareness, mobile technologies and emergency management. After identified the limitations of these works, we have proposed a mobile context-aware information system that utilizes mobile technologies and context awareness to provide relevant emergency instructions to public users. Based on this system, we also described case studies that have been conducted to validate this system. Our motivation of this work is to fill in the gap of using context awareness and mobile technology for more efficient and effective emergency management and also provide reference for similar applications in other domains.

Index Terms—Emergency management, mobile information system, context awareness, mobile software, XML.

Nan Jing is with the School of Management, Harbin Institute of Technology, Harbin, 150001, China (e-mail: jingnan@gmail.com).

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Cite: Nan Jing, "Skyguard: A Context-Aware Mobile Information System for Emergency Management," International Journal of Machine Learning and Computing vol. 5, no. 1, pp. 44-50, 2015.

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