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2015 | 5 | 67--74
Tytuł artykułu

DISESOR - Decision Support System for Mining Industry

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.(original abstract)
Rocznik
Tom
5
Strony
67--74
Opis fizyczny
Twórcy
  • Institute of Informatics, Silesian University of Technology , Poland
autor
  • Institute of Informatics, Silesian University of Technology , Poland
  • Institute of Informatics, Silesian University of Technology , Poland
Bibliografia
  • M. Sikora and B. Sikora, "Rough natural hazards monitoring," in Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, 2012, pp. 163-179. [Online]. Available: http://dx.doi. org/10.1007/978-1-4471-2760-4_10
  • P. Kadlec, B. Gabrys, and S. Strandt, "Data-driven soft sensors in the process industry," Computers & Chemical Engineering, vol. 33, no. 4, pp. 795-814, 2009. doi: 10.1016/j.compchemeng.2008.12.012. [Online]. Available: http://dx.doi.org/10.1016/j.compchemeng.2008.12. 012
  • M. Sikora and B. Sikora, "Improving prediction models applied in systems monitoring natural hazards and machinery," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 2, pp. 477- 491, 2012. doi: 10.2478/v10006-012-0036-3. [Online]. Available: http://dx.doi.org/10.2478/v10006-012-0036-3
  • A. Lesniak and Z. Isakow, "Space-time clustering of ´ seismic events and hazard assessment in the zabrzebielszowice coal mine, poland," International Journal of Rock Mechanics and Mining Sciences, vol. 46, no. 5, pp. 918-928, 2009. doi: 10.1016/j.ijrmms.2008.12.003. [Online]. Available: http://dx.doi.org/10.1016/j.ijrmms. 2008.12.003
  • J. Kabiesz, "Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks," Geotechnical & Geological Engineering, vol. 24, no. 5, pp. 1131-1147, 2006. doi: 10.1007/s10706-005- 1136-8. [Online]. Available: http://dx.doi.org/10.1007/ s10706-005-1136-8
  • J. Kabiesz, B. Sikora, M. Sikora, and Ł. Wróbel, "Application of rule-based models for seismic hazard prediction in coal mines," ACTA MONTANISTICA SLOVACA, vol. 18, no. 4, pp. 262-277, 2013.
  • R. Kimball and M. Ross, The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons, 2011.
  • S. T. March and A. R. Hevner, "Integrated decision support systems: A data warehousing perspective," Decision Support Systems, vol. 43, no. 3, pp. 1031- 1043, 2007. doi: 10.1016/j.dss.2005.05.029. [Online]. Available: http://dx.doi.org/10.1016/j.dss.2005.05.029
  • M. Michalak, M. Sikora, and J. Sobczyk, "Analysis of the longwall conveyor chain based on a harmonic analysis," Eksploatacja i Niezawodno´s´c - Maintenance and Reliability, vol. 15, no. 4, pp. 332-333, 2013.
  • M. Kalisch, P. Przystalka, and A. Timofiejczuk, "Application of selected classification schemes for fault diagnosis of actuator systems," in Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on. IEEE, 2014. doi: 10.15439/2014F158 pp. 1381-1390. [Online]. Available: http://dx.doi.org/10. 15439/2014F158
  • M. Grzegorowski, "Scaling of complex calculations over big data-sets," in Active Media Technology. Springer, 2014, pp. 73-84. [Online]. Available: http: //dx.doi.org/10.1007/978-3-319-09912-5_7
  • E. Kozan and S. Q. Liu, "A demand-responsive decision support system for coal transportation," Decision Support Systems, vol. 54, no. 1, pp. 665-680, 2012. doi: 10.1016/j.dss.2012.08.012. [Online]. Available: http: //dx.doi.org/10.1016/j.dss.2012.08.012
  • RapidMiner. (2015) Rapidminer. [Online]. Available: http://rapidminer.com
  • R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2014. [Online]. Available: http://www.R-project.org
  • A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, "Moa: Massive online analysis," The Journal of Machine Learning Research, vol. 11, pp. 1601-1604, 2010.
  • Sevitel. (2015) Thor. [Online]. Available: http://www. sevitel.pl/product,25,THOR.html
  • Talend. (2015) Talend open studio. [Online]. Available: https://www.talend.com/products/talend-open-studio
  • PostgreSQL. (2015) Postgresql. [Online]. Available: http://www.postgresql.org/
  • T. Amin, I. Chikalov, M. Moshkov, and B. Zielosko, "Relationships between length and coverage of decision rules," Fundam. Inform., vol. 129, no. 1-2, pp. 1-13, 2014. doi: 10.3233/FI-2014-956. [Online]. Available: http://dx.doi.org/10.3233/FI-2014-956
  • U. Stanczyk, "Decision rule length as a basis for evaluation of attribute relevance," Journal of Intelligent and Fuzzy Systems, vol. 24, no. 3, pp. 429-445, 2013. doi: 10.3233/IFS-2012-0564. [Online]. Available: http://dx.doi.org/10.3233/IFS-2012-0564
  • L. S. Riza, A. Janusz, C. Bergmeir, C. Cornelis, F. Herrera, D. Sl˛ezak, J. M. Benítez ´ et al., "Implementing algorithms of rough set theory and fuzzy rough set theory in the r package "roughsets"," Information Sciences, vol. 287, pp. 68-89, 2014. doi: 10.1016/j.ins.2014.07.029. [Online]. Available: http://dx.doi.org/10.1016/j.ins.2014. 07.029
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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