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—In this paper, we summarize AAIA'15 data mining competition: Tagging Firefighter Activities at a Fire Scene, which was held between March 9 and July 6, 2015. We describe the scope and background of the competition. We also reveal details regarding the data set used in the competition, which was collected and tagged specifically for the purpose of this data(More)
We discuss how to support commanders at the fire ground. We present a risk management framework for modelling the fire phenomena and for communicating with firefighters. We claim that appropriate derivation, selection and representation of information is the crucial aspect of decision support, as it may improve the commander's perception.
We describe the scope and background of this competition and we explain in details the evaluation procedure. We also briefly overview the results of this analytical challenge, showing the way in which those results can be beneficial to one of our other projects which is related to the problem of improving firefighter safety at a fire scene. Finally, we(More)
—In the article we present a comparison of the classification algorithms focused on labeling Fire&Rescue incidents with threats appearing at the emergency scene. Each of the incidents is reported in a database and characterized by a set of quantitative attributes and by natural language descriptions of the cause, the scene and the course of actions(More)
In the article we present the comparison of the information retrieval approaches focused on a searching of specific concepts in a Natural Language part of Fire Service reports. The comparison comprise of searching with use of regular expressions, Latent Semantic Indexing and pre-defined set of search terms. As a case study we selected three concepts which(More)