From Sensory Data to Decision Making: A Perspective on Supporting a Fire Commander

Krasuski, Adam; Jankowski, Andrzej; Skowron, Andrzej; Ślęzak, Dominik

doi:10.1109/wi-iat.2013.188

Cited by 26 publications

(18 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In dynamically changing situations, such as those occurring at a fire scene, all decisions need to be taken in a very short time [6]. Several initiatives and research projects analyze various aspect of this complex problem [6,7,8]. The lack of situational awareness is listed there as the main factor associated with major accidents among firefighters.…”

Section: Problem Descriptionmentioning

confidence: 99%

Robust histogram-based feature engineering of time series data

Zdravevski

Lameski

Mingov³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

Robust histogram-based feature engineering of time series data

Zdravevski

Lameski

Mingov³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Such rules may compose a useful tool for supporting ICs at a fire ground, which is the main task of our ICRA project [2].…”

Section: Attribute Cluster Analysismentioning

confidence: 99%

“…The information is gathered in order to calculate statistics within the groups of incidents, identify peculiar cases and to improve the procedures [1]. Results of a thorough analysis of incident reports can also be utilized by decision support systems to increase safety of firefighters at a fire scene [2].…”

mentioning

confidence: 99%

Key Risk Factors for Polish State Fire Service: a Data Mining Competition at Knowledge Pit

Janusz¹,

Krasuski²,

Stawicki³

et al. 2014

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper we summarize AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service which was held between February 3, 2014 and May 5, 2014 at the Knowledge Pit platform http://challenge.mimuw.edu.pl/. 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 reveal some technical details regarding the architecture and functionalities of the Knowledge Pit competition platform, which we are developing in order to facilitate solving of practical problems that require advanced data analytics.

show abstract

“…The identification of key risk factors for casualties among firefighters, children and other people involved was a topic of data mining competition organized within the 9th International Symposium on Advances in Artificial Intelligence and Applications (AAIA) and was an integral part of the 1st Complex Events and Information Modelling workshop devoted to fire protection engineering. The competition results will bring data-driven insights into key risk factors in incidents and contribute to safety improvement, which is important for Fire Service supporting systems [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Risk Factors for the Polish State Fire Service with Cascade Step Forward Feature Selection

Płoński

2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The Polish State Fire Service gathers information about incidents which require their intervention. This information is stored to document the events. However, it can be very useful for new officers training, better identification of threats and planning of more effective procedures. The identification of key risk factors for casualties among firefighters, children or other involved people was a topic of data mining competition organized as a part of 1st Complex Events and Information Modelling workshop devoted to the fire protection engineering. The task of the competition was to find ten subsets of features for ten Naive Bayes classifiers. The ensemble output was used to predict occurence of casualities. Herein, the solution description that took 5th place is presented. The proposed method used cascade step forward feature selection procedure to find features subsets.

show abstract

From Sensory Data to Decision Making: A Perspective on Supporting a Fire Commander

Cited by 26 publications

References 12 publications

Robust histogram-based feature engineering of time series data

Robust histogram-based feature engineering of time series data

Key Risk Factors for Polish State Fire Service: a Data Mining Competition at Knowledge Pit

Identification of Key Risk Factors for the Polish State Fire Service with Cascade Step Forward Feature Selection

Contact Info

Product

Resources

About