Systematic Mapping Approach to Support Administration Quantitative Research
Abstract
This paper explores guidelines for the execution of quantitative research on a specific topic. Using a systematic mapping, as explained here, it is possible to obtain large numbers of scientific publications and properly analyze them to achieve a better understanding of state-of-the-art research, overall topic maturity, and its relevance through time. Additionally, systematic mappings provide answers to research questions as defined by the researcher conducting the process, allowing for further exploration of specific knowledge concerning the studied domain. In this study, the topic of 4.0 Industry is analyzed to illustrate the process and its results.
Downloads
References
Kitchenham, B.; Charters, S.. Guidelines for performing Systematic Literature Reviews in Software Engineering (Version 2.3) - EBSE Technical Report. EBSE-2007-0, 2007.
Kitchenham B. A.; Pretorius R.; Budgen D.; Pearl Brereton O.; Turner M.; Niazi M.; Linkman S.. Systematic literature reviews in software engineering – A tertiary study. Inf. Softw. Technol., vol. 52, no. 8, pp. 792–805, 2010. doi: 10.1016/j.infsof.2010.03.006.
Nonato, F. S.; Leal, A. L. C.; Fortes, M. Z.. Indústria 4.0 no Monitoramento e Controle da Produção: Um Estudo por Mapeamento Sistemático. Gestão em foco - UNISEPE, pp. 127-161, 2020.
Mubeen, S.; Asadollah, S. A.; Papadopoulos, A. V.; Ashjaei, M.; Pei-Breivold, H.; Behnam, M.. Management of service level agreements for cloud services in IoT: A systematic mapping study. IEEE Access, 6, 30184-30207, 2017. doi: 10.1109/ACCESS.2017.2744677.
Kitchenham B. A.; Pfleeger S. L.; Pickard L. M.; Jones P. W.; Hoaglin D. C.; El Emam K.; Rosenberg J.. Preliminary guidelines for empirical research in software engineering, IEEE Trans. Softw. Eng., vol. 28, no. 8, pp. 721–734, 2002. doi: 10.1109/TSE.2002.1027796.
Valença, G.; Alves, C.; Alves, V.; Niu, N.. A systematic mapping study on business process variability. International Journal of Computer Science & Information Technology, vol.5, no.1, 2013. doi: 10.5121/ijcsit.2013.5101.
Petersen K.; Feldt R.; Mujtaba S.; Mattsson M.. Systematic Mapping Studies in Software Engineering, in Proc. of the 12th international conference on Evaluation and Assessment in Software Engineering, 2008, pp. 68–77, 2008.
Ross D.; Schoman A.. Structured analysis for requirements definition. IEEE Trans Software Engineering, vol.3, no.1, pp.6–15, 1977. doi: 10.1109/TSE.1977.229899.
Chen, B.; Wan, J., Shu, L.; Li, P.; Mukherjee, M.; Yin, B.. Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505-6519, 2017. DOI: 10.1109/ACCESS.2017.2783682.
Gaggero, M.; Di Paola, D.; Petitti, A.; Caviglione, L. When Time Matters: Predictive Mission Planning in Cyber-Physical Scenarios. IEEE Access, 7, 11246-11257, 2019. DOI: 10.1109/ACCESS.2019.2892310.
He, Z.; He, Y.; Liu, F.; Zhao, Y.. Big data-oriented product infant failure intelligent root cause identification using associated tree and fuzzy DEA. IEEE Access, 7, 34687-34698, 2019. DOI: 10.1109/ACCESS.2019.2904759.
Jenderny, S.; Foullois, M.; Kato-Beiderwieden, A. L.; Bansmann, M.; Wöste, L.; Lamß, J.; Röcker, C.. Development of an instrument for the assessment of scenarios of work 4.0 based on socio-technical criteria, in Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, pp. 319-326, 2018. DOI: 10.1145/3197768.3201566.
Ostermeyer, E.; Danjou, C.; Durupt, A.; Le Duigou, J.. An ontology-based framework for the management of machining information in a data mining perspective. IFAC-PapersOnLine, vol.51, no.11, 302-307, 2018. DOI: 10.1016/j.ifacol.2018.08.300.
Liu, D.; Liao, H.; Zio, E.; Miao, Q.; Zhang, B. Hu, C.; Azarian, M.H..: Complex System Health Management Based on Condition Monitoring and Test Data. IEEE Access, 6, 72028-72032, 2018.
Yang, W.; Takakuwa, S. Simulation-based dynamic shop floor scheduling for a flexible manufacturing system in the industry 4.0 environment, iIn 2017 Winter Simulation Conference (WSC), pp. 3908-3916, 2017. DOI: 10.1109/WSC.2017.8248101.
Akoka, J.; Comyn-Wattiau, I.; Laoufi, N. Research on Big Data–A systematic mapping study. Computer Standards & Interfaces, 54, 105-115, 2017. DOI: 10.1016/j.csi.2017.01.004.
Galletta, A.; Carnevale, L.; Celesti, A.; Fazio, M.; Villari, M. A cloud-based system for improving retention marketing loyalty programs in industry 4.0: a study on big data storage implications. IEEE Access, 6, 5485-5492, 2017. DOI: 10.1109/ACCESS.2017.2776400.
Illa, P. K.; Padhi, N. Practical Guide to Smart Factory Transition Using IoT, Big Data and Edge Analytics. IEEE Access, 6, 55162-55170, 2018. DOI: 10.1109/ACCESS.2018.2872799.
Li, X.; Li, D.; Li, S.; Wang, S.; Liu, C.. Exploiting Industrial Big Data Strategy for Load Balancing in Industrial Wireless Mobile Networks. IEEE Access, 6, 6644-6653, 2017. DOI: 10.1109/ACCESS.2017.2787978.
Wan, J.; Hong, J.; Pang, Z.; Jayaraman, B.; Shen, F. Key technologies for smart factory of Industry 4.0. IEEE Access, 7, 17969-17974, 2017.
Lopez, C. P.; Segura, M.; Santórum, M. Data Analytics and BI Framework based on Collective Intelligence and the Industry 4.0, in Proceedings of the 2019 2nd International Conference on Information Science and Systems, pp. 93-98, 2019. DOI: 10.1145/3322645.3322667.
Xu, X.; Hua, Q.. Industrial big data analysis in smart factory: Current status and research strategies. IEEE Access, 5, 17543-17551, 2017. DOI: 10.1109/ACCESS.2017.2741105
Yan, H.; Wan, J.; Zhang, C.; Tang, S.; Hua, Q.; Wang, Z.. Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access, 6, 17190-17197, 2018. DOI: 10.1109/ACCESS.2018.2809681.
This work is licensed under a Creative Commons Attribution 4.0 International License.
