PL EN


2017 | 3(49) | 109-124
Article title

The Architecture of the Intelligent Case-Based Reasoning Recommender System (CBR RS) Recommending Preventive/Corrective Procedures in the Occupational Health and Safety Management System in an Enterprise

Authors
Title variants
PL
Architektura inteligentnego systemu klasy CBR RS (Case-Based Reasoning Recommender System) rekomendującego procedury zapobiegawczo-korygujące w systemie BHP przedsiębiorstwa
Languages of publication
EN
Abstracts
EN
The paper presents the original architecture of the system recommending preventive/corrective procedures in the occupational health and safety management system in an enterprise: ComplianceOHS-CBR. The system consists of four modules: Module A — an ontology of the workplace OHS profile, Module B — an ontology of preventive/corrective procedure indexation OPCPI, Module C — a recording system of the monitoring process of non-compliance with the requirements of OHS, Module D — a recommending engine consistent with the CBR methodology. The essence of the approach presented in this paper is integration of the monitoring system of the analysis process of non-compliance with the requirements of OHS at the workplace (the ADONIS system was used) with the case-based reasoning process (CBR). The integration platform consists of two ontologies: an ontology of profile compliance with the workplace OHS requirements (OP-OHS) and an ontology of preventive/corrective procedure indexation (OPCPI). Both of the ontologies are presented in the Protege 5 OWL editor. Inference engines are alternatively, according to the CBR methodology, myCBR and jCOLLIBRI.
PL
W pracy przedstawiono oryginalną architekturę systemu rekomendującego procedury zapobiegawczo-korygujące w systemie BHP przedsiębiorstwa: Compliance OHS-CBR. System składa się z czterech modułów: moduł A: ontologia profilu BHP stanowiska pracy, moduł B: ontologia indeksacji procedur zapobiegawczo-korygujących OIP-ZK, moduł C: system ewidencjonowania procesu monitorowania niezgodności z wymaganiami BHP, moduł D: silnik wydawania rekomendacji w metodologii CBR. Istotą podejścia prezentowanego w niniejszej pracy jest integracja systemu monitorowania procesu analizy niezgodności z wymaganiami BHP na stanowiskach pracy (zastosowano oprogramowanie ADONIS) z systemem wnioskowania z bazy przypadków CBR. Platformą integracji są dwie ontologie: ontologia profilu zgodności z wymaganiami BHP na stanowisku pracy (OP-BHP) oraz ontologia indeksacji procedur zapobiegawczo--korygujących OIP-ZK. Obydwie ontologie przedstawiono w edytorze Protege 5 języka OWL. Silnikami wnioskującymi zgodnie z metodologią CBR są alternatywnie: myCBR oraz jCOLLIBRI.
Contributors
author
  • University of Information Technology and Management in Rzeszow
References
  • Aamodt, A., and E. Plaza. 1994. “Case-Based Reasoning — Foundational Issues, Methodological Variations, and System Approaches.” Ai Communications 7 (1): 39–59.
  • Amailef, K., and J. Lu. 2013. “Ontology-Supported Case-Based Reasoning Approach for Intelligent m-Government Emergency Response Services.” Decision Support Systems 55 (1): 79–97. doi: 10.1016/j.dss.2012.12.034.
  • Andreasik, J. 2015. “Koncepcja ontologii systemu bezpieczeństwa i higieny pracy.” Barometr Regionalny. Analizy i Prognozy 13 (3): 179–189.
  • Bergmann, R., J. Kolodner, and E. Plaza. 2005. “Representation in Case-Based Reasoning.” Knowledge Engineering Review 20 (3): 209–213. doi: 10.1017/S0269888906000555.
  • Bobadilla, J., F. Ortega, A. Hernando, and A. Gutierrez. 2013. “Recommender Systems Survey.” Knowledge-Based Systems 46: 109–132. doi: 10.1016/j.knosys.2013.03.012.
  • Dendani-Hadiby, N., and M.T. Khadir. 2013. “A Fault Diagnosis Application Based on a Combination Case-Based Reasoning and Ontology Approach.” International Journal of Knowledge-Based and Intelligent Engineering Systems 17 (4): 305–317. doi: 10.3233/KES-130280.
  • Dietz, J.L.G. 2006. Enterprise Ontology. Theory and Methodology. Berlin – New York: Springer.
  • El-Sappagh, S.H., and M. Elmogy. 2015. “Case Based Reasoning: Case Representation Methodologies.” International Journal of Advanced Computer Science and Applications 6 (11): 192–208.
  • Gawin, B., and B. Marcinkowski. 2013. Symulacja procesów biznesowych. Standardy BPMS i BPMN w praktyce, Onepress. Gliwice: Helion.
  • Hinkelmann, K., A. Gerber, D. Karagiannis, B. Thoenssen, A. van der Merwe, and R. Woitsch. 2016. “A New Paradigm for the Continuous Alignment of Business and IT: Combining Enterprise Architecture Modelling and Enterprise Ontology.” Computers in Industry 79: 77–86. doi: 10.1016/j.compind.2015.07.009.
  • Kaplan, R.S., and D.P. Norton. 1996. The Balanced Scorecard. Translating Strategy into Action. Boston, Mass.: Harvard Business School Press.
  • Lu, J., D.S. Wu, M.S. Mao, W. Wang, and G.Q. Zhang. 2015. “Recommender System Application Developments: a Survey.” Decision Support Systems 74: 12–32. doi: 10.1016/j.dss.2015.03.008.
  • Lu, Y., Q.M. Li, and W.J. Xiao. 2013. “Case-Based Reasoning for Automated Safety Risk Analysis on Subway Operation: Case Representation and Retrieval.” Safety Science 57: 75–81. doi: 10.1016/j.ssci.2013.01.020.
  • Ly, L.T., F.M. Maggi, M. Montali, S. Rinderle-Ma, and W.M.P. van der Aalst. 2015. “Compliance Monitoring in Business Processes: Functionalities, Application, and Tool-Support.” Information Systems 54: 209–234. doi: 10.1016/j.is.2015.02.007.
  • Rao, S.S., and A. Nayak. 2017. “Enterprise Ontology Model for Tacit Knowledge Externalization in Socio-Technical Enterprises.” Interdisciplinary Journal of Information, Knowledge, and Management 12: 99–124.
  • Recio-Garcia, J.A., P.A. Gonzalez-Calero, and B. Diaz-Agudo. 2014. “jCOLIBRI2: A Framework for Building Case-Based Reasoning Systems.” Science of Computer Programming 79: 126–145. doi: 10.1016/j.scico.2012.04.002.
  • Rintala, L., M. Leikola, C. Sauer, J. Aromaa, T. Roth-Berghofer, O. Forsen, and M. Lundstrom. 2017. “Designing Gold Extraction Processes: Performance Study of a Case-Based Reasoning System.” Minerals Engineering 109: 42–53. doi: 10.1016/j.mineng.2017.02.013.
  • Saracino, A., G. Antonioni, G. Spadoni, D. Guglielmi, E. Dottori, L. Flamigni, M. Malagoli, and V. Pacini. 2015. “Quantitative Assessment of Occupational Safety and Health: Application of a General Methodology to an Italian Multi-Utility Company.” Safety Science 72: 75–82. doi: 10.1016/j.ssci.2014.08.007.
  • Sauer, C. 2016. Knowledge Elicitation and Formalisation for Context and Explanation-Aware Computing with Case-Based Recommender Systems. Doctoral thesis, University of West London, London.
  • Sauer, C., A. Kheirkhahzadeh, and T. Roth-Berghofer. 2016. Data Literacy in the Smart University Approach. Paper read at Learning Analytics and Knowledge Conference 2016, 2016.04.26, at Edinburgh, UK.
  • Seck, M., and J. Barjis. 2015. “An Agent Based Approach for Simulating DEMO Enterprise Models.” Complex Adaptive Systems, 2015 61: 246–253. doi: 10.1016/j.procs.2015.09.206.
  • Teimourikia, M., and M. Fugini. 2017. “Ontology Development for Run-Time Safety Management Methodology in Smart Work Environments Using Ambient Knowledge.” Future Generation Computer Systems — the International Journal of Escience 68: 428–441. doi: 10.1016/j.future.2016.07.003.
  • Virkki-Hatakka, T., and G.L.L. Reniers. 2009. “A Case-Based Reasoning Safety Decision-Support Tool: Nextcase/Safety.” Expert Systems with Applications 36 (7): 10374–10380. doi: 10.1016/j.eswa.2009.01.059.
  • Yahia, Z., J. Iijima, N.A. Harraz, and A.B. Eltawil. 2017. “A Design and Engineering Methodology for Organization-Based Simulation Model for Operating Room Scheduling Problems.” Simulation-Transactions of the Society for Modeling and Simulation International 93 (5): 363–378. doi: 10.1177/0037549716687376.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-c7aafa12-63d5-4ce6-90c7-2fbf331444fb
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.