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Han Che, & Shuming Liu. (2013). Monitoring data identification for a water distribution system based on data self-recognition approach. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 166–170). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.
Keywords: Information systems; Random processes; Statistics; Water distribution systems; ARMA model; Autoregressive moving average model; Contamination events; Data identification; Outlier identification; Prediction confidence; Self-recognition; Water distribution networks; Monitoring
Track: Critical Infrastructures
Shuming Liu, Christopher Brewster, & Duncan Shaw. (2013). Ontologies for crisis management: A review of state of the art in ontology design and usability. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 349–359). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The growing use of a variety of information systems in crisis management both by non-governmental organizations (NGOs) and emergency management agencies makes the challenges of information sharing and interoperability increasingly important. The use of semantic web technologies is a growing area and is a technology stack specifically suited to these challenges. This paper presents a review of ontologies, vocabularies and taxonomies that are useful in crisis management systems. We identify the different subject areas relevant to crisis management based on a review of the literature. The different ontologies and vocabularies available are analysed in terms of their coverage, design and usability. We also consider the use cases for which they were designed and the degree to which they follow a variety of standards. While providing comprehensive ontologies for the crisis domain is not feasible or desirable there is considerable scope to develop ontologies for the subject areas not currently covered and for the purposes of interoperability.
Keywords: Information systems; Interoperability; Ontology; Risk management; Semantic Web; Societies and institutions; Crisis management; Crisis management systems; Emergency management; Information interoperability; Information sharing; Nongovernmental organizations; Ontology design; Semantic Web technology; Management information systems