DISESOR - decision support system for mining industry

This paper presents the DISESOR integrated decision support system. The system integrates data from different monitoring and dispatching systems and contains such modules as data preparation and cleaning, analytical, prediction and expert system. Architecture of the system is presented in the paper and a special focus is put on the presentation of two issues: data integration and cleaning, and creation of prediction model. The work contains also a case study presenting an example of the system application.

[1]  Κωνσταντίνος Φαρδέλας,et al.  Οδηγός Χρήσης Πλατφόρμας Talend Open Studio , 2012 .

[2]  Yiyu Yao,et al.  Rough Sets: Selected Methods and Applications in Management and Engineering , 2012, Advanced Information and Knowledge Processing.

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

[4]  Geoff Holmes,et al.  MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..

[5]  Alan R. Hevner,et al.  Integrated decision support systems: A data warehousing perspective , 2007, Decis. Support Syst..

[6]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling , 1996 .

[7]  Marcin Michalak,et al.  Analysis of the longwall conveyor chain based on a harmonic analysis , 2013 .

[8]  Marek Grzegorowski,et al.  Scaling of Complex Calculations over Big Data-Sets , 2014, AMT.

[9]  Józef Kabiesz,et al.  Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks , 2006 .

[10]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[11]  Igor Chikalov,et al.  Relationships Between Length and Coverage of Decision Rules , 2014, Fundam. Informaticae.

[12]  Urszula Stanczyk,et al.  Decision rule length as a basis for evaluation of attribute relevance , 2013, J. Intell. Fuzzy Syst..

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

[14]  Erhan Kozan,et al.  A demand-responsive decision support system for coal transportation , 2012, Decis. Support Syst..

[15]  Andrzej Lesniak,et al.  Space–time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland , 2009 .

[16]  Marek Sikora,et al.  Improving prediction models applied in systems monitoring natural hazards and machinery , 2012, Int. J. Appl. Math. Comput. Sci..

[17]  Agnieszka Lawrynowicz,et al.  Pattern Based Feature Construction in Semantic Data Mining , 2014, Int. J. Semantic Web Inf. Syst..

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

[19]  Mateusz Kalisch,et al.  Application of selected classification schemes for fault diagnosis of actuator systems , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[20]  Beata Sikora,et al.  Rough Natural Hazards Monitoring , 2012 .