Understanding and Using Context
Personal and Ubiquitous Computing
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Public warning in the networked age: open standards to the rescue?
Communications of the ACM - Emergency response information systems: emerging trends and technologies
Semantics and complexity of SPARQL
ACM Transactions on Database Systems (TODS)
It's a Streaming World! Reasoning upon Rapidly Changing Information
IEEE Intelligent Systems
Crowd-sourced sensing and collaboration using twitter
WOWMOM '10 Proceedings of the 2010 IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)
Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
Ad-hoc ride sharing application using continuous SPARQL queries
Proceedings of the 21st international conference companion on World Wide Web
Communications of the ACM
Windowing mechanisms for web scale stream reasoning
Proceedings of the 4th international workshop on Web-scale knowledge representation retrieval and reasoning
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Public alert services is gradually becoming popular in smart cities because this enhances the awareness of the citizen about activities within the city. Such a service also ensures the safety and security of the citizens. However the state of the art lacks in providing real-time alerts in a personalized, context-aware fashion utilizing the combined knowledge about the city, its events and its citizens. In this paper, a solution architecture is presented that uses stream reasoning as its backbone which suits the domain of a public alert system very well. The stream reasoner uses rule-based reasoning and queries. The rules are designed as atomic concepts. A fully functional prototype of the proposed system was developed and tested on data of a smart city. The experimental results support that the proposed methodology is very effective.