Mining Data from Coal Mines: IJCRS'15 Data Challenge

We summarize the data mining competition associated with IJCRS’15 conference – IJCRS’15 Data Challenge: Mining Data from Coal Mines, organized at Knowledge Pit web platform. The topic of this competition was related to the problem of active safety monitoring in underground corridors. In particular, the task was to design an efficient method of predicting dangerous concentrations of methane in longwalls of a Polish coal mine. We describe the scope and motivation for the competition. We also report the course of the contest and briefly discuss a few of the most interesting solutions submitted by participants. Finally, we reveal our plans for the future research within this important subject.

[1]  Andrzej Skowron,et al.  From Sensory Data to Decision Making: A Perspective on Supporting a Fire Commander , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[2]  Marc Boullé Tagging fireworkers activities from body sensors under distribution drift , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[3]  Marek Sikora,et al.  Application of rule-based models for seismic hazard prediction in coal mines , 2014 .

[4]  Hung Son Nguyen,et al.  On Efficient Handling of Continuous Attributes in Large Data Bases , 2001, Fundam. Informaticae.

[5]  Francisco Herrera,et al.  Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets" , 2014, Inf. Sci..

[6]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[7]  Michal Kozielski,et al.  Regression Rule Learning for Methane Forecasting in Coal Mines , 2015, BDAS.

[8]  Marek Grzegorowski,et al.  Window-based feature extraction framework for multi-sensor data: A posture recognition case study , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[9]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[10]  Dominik Slezak,et al.  Key risk factors for Polish State Fire Service: A Data Mining Competition at Knowledge Pit , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[11]  Andrzej Janusz,et al.  Algorithms for Similarity Relation Learning from High Dimensional Data , 2014, Trans. Rough Sets.

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .