Mining Data from Coal Mines: IJCRS’15 Data Challenge

Janusz, Andrzej; Sikora, Marek; Wróbel, Łukasz; Stawicki, Sebastian; Grzegorowski, Marek; Wojtas, P.; Ślęzak, Dominik

doi:10.1007/978-3-319-25783-9_38

Cited by 21 publications

(7 citation statements)

References 9 publications

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“…The data set in a processed form was used in a data analysis competition [2] . In this case data was transformed into 51,700 time periods of 10 min each.…”

Section: Data Descriptionmentioning

confidence: 99%

Data on methane concentration collected by underground coal mine sensors

2021

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Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions. To provide protection for people working underground, systems for active monitoring of production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) to prevent spontaneous explosions. Such a system is the source of the data set containing raw data collected at an underground coal mine. The data is collected from 28 different sensors placed at various locations around the coal mine. All the attributes except one are numeric, and the examples collected form a time series. This data set can be used in a variety of analytical tasks, including classification, regression, time series and stream data analysis.

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“…The data set in a processed form was used in a data analysis competition [2] . In this case data was transformed into 51,700 time periods of 10 min each.…”

Section: Data Descriptionmentioning

confidence: 99%

Data on methane concentration collected by underground coal mine sensors

2021

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“…Hence, we started with 1,060 rows in total and increased it up to 83,460 rows (160 iterations). We used the values in IJCRS 15 Data Challenge: Mining Data from Coal Mines (Janusz et al, ) to generate values for gas levels realistically. However, this dataset does not contain any events risking OHS.…”

Section: Case Study: Health and Safety In Minesmentioning

confidence: 99%

Enabling smart environments through scalable policy reasoning and Internet of Things

Göynügür

Şensoy

Mel

2018

WIREs Data Min & Knowl

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In this paper, we discuss how to combine ontology-based policy reasoning mechanisms with in-use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that it is scalable under high load of data and devices.

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“…The first, concerned the recognition of the activity and posture of firefighter based on readings from multiple motion and vital sensors as a part of AAIA'15 Data Mining Competition: Tagging Firefighter Activities at the Fire Scene [17]. The second concerned the prediction of dangerous concentration of methane in the atmosphere of the mine sidewalks as a part of IJCRS'15 Data Challenge: Mining Data from Coal Mines [10]. The third concerned the prediction of increased seismic activity in mines as a part of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines [9].…”

Section: Introductionmentioning

confidence: 99%

Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events

Grzegorowski

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed framework to build an ensemble of classifiers which takes into account both the quality and the diversity of individual models. The effectiveness of the solution has been verified through a participation in an open data mining competition which concerned the problem of predicting periods of increased seismic activity causing lifethreatening accidents in coal mines. The submitted solution obtained the highest AUC score of all the solutions uploaded by 106 participating research teams.

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Mining Data from Coal Mines: IJCRS’15 Data Challenge

Cited by 21 publications

References 9 publications

Data on methane concentration collected by underground coal mine sensors

Data on methane concentration collected by underground coal mine sensors

Enabling smart environments through scalable policy reasoning and Internet of Things

Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events

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