Abstract: Increasing market volatility and disruptions imply risks for companies and governments and have become therefore focus topics. Adequate tools to identify, assess and manage future developments are key to survive in a turbulent environment. In our paper, we present the systematic development process of an innovative, web-based foresight platform, which is a joint research project funded by the German Federal Government and aims to improve the robustness in decision making by collaborative foresight. Its four interlinked applications have the purpose to enable their users a collaborative generation, discussion, evaluation and development of future-oriented knowledge. Thereby, a special emphasis is on the relevance and the timeliness of the provided information. Within the multi-stage requirement analysis of the tool platform we analyzed existing concepts in order to identify strengths and weaknesses and conducted brainstorming sessions and interviews with professionals of 130 companies and organizations to account for different backgrounds, perspectives and intentions. © 2012 ISCRAM.

Abstract: Epidemiological monitoring systems are used to control the evolution of disease spreading and to suggest action plans to prevent identified risks. In this domain, risk prediction is based on quantitative approaches that are hardly usable when data collection is not possible. In this paper, a qualitative approach based on an epidemiological monitoring ontology is proposed. We describe the design of this ontology and show how it fits into classical monitoring systems and helps overcoming limits related to quantitative approaches. © 2012 ISCRAM.

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.

Abstract: In case of indoor fire hazards, firefighters face difficulties at assessing the fire situation and evacuating trapped victim inside the building, especially when the fire is big, and the building is unknown to them. On the other hand, modern sensor technologies in smartphone are becoming more advanced, widespread, and can be exploited for helping the firefighting operation. This paper proposes using smartphones as a distributed sensing and computing platform, for supporting firefighters to conduct their mission. The developed solution is based on collecting sensor data from smartphones. A Bayesian network then uses this data to generate a picture of the fire and predict its development. The additional indoor positioning feature make this proposed solution a promising tool to make the firefighter intervention more efficient and fast in order to save more lives.

Abstract: During the past quarter century there have been many developments in scientific models and computer codes to help predict the ongoing consequences in the aftermath of many types of emergency: e.g. storms and flooding, chemical and nuclear accident, epidemics such as SARS and terrorist attack. Some of these models relate to the immediate events and can help in managing the emergency; others predict longer term impacts and thus can help shape the strategy for the return to normality. But there are many pitfalls in the way of using these models effectively. Firstly, non-scientists and, sadly, many scientists believe in the models' predictions too much. The inherent uncertainties in the models are underestimated; sometimes almost unacknowledged. This means that initial strategies may need to be revised in ways that unsettle the public, losing their trust in the emergency management process. Secondly, the output from these models form an extremely valuable input to the decision making process; but only one such input. Most emergencies are events that have huge social and economic impacts alongside the health and environmental consequences. While we can model the latter passably well, we are not so good at modelling economic impacts and very poor at modelling social impacts. Too often our political masters promise the best 'science-based' decision making and too late realise that the social and economic impacts need addressing. In this paper, we explore how model predictions should be drawn into emergency management processes in more balanced ways than often has occurred in the past. © Proceedings ISCRAM 2004.

Abstract: Emergencies are characterised by uncertainty. This motivates the design of information systems that model and predict complex natural, material or human processes to support understanding and reduce uncertainty through prediction. The correspondence between system models and reality, however, is also governed by uncertainties, and designers have developed methods to render ?the world? transparent in ways that can inform, fine-tune and validate models. Additionally, people experience uncertainties in their use of simulation and prediction systems. This is a major obstacle to effective utilisation. We discuss ethically and socially motivated demands for transparency.

Abstract: Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.