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Text Mining the Variety of Trends in the Field of Simulation Modeling Research

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Perspectives in Business Informatics Research (BIR 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 398))

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Abstract

Simulation modeling as a research field is engaging and multidisciplinary, drawing on a wide range of techniques and tools. It has reached its 60th anniversary amassing to an enormous corpus of literature. To date, many studies explored its advancements in specific disciplines. This paper aims to present a more structured and cross-disciplinary approach by identifying trends and extracting patterns from text files using text mining techniques. For that purpose, abstracts from over 5,000 papers were downloaded from the WoS database and analyzed. The results are complemented and visualized through the process of science mapping and positioned to existing frameworks (Plan-Do-Check-Act and Bloom’s taxonomy in particular). The source with the most journal papers related to simulation modeling has been Ecological modelling; however, recent research focus has shifted to healthcare issues. Overall, the results confirm the relevancy of the simulation modeling field and acknowledge the trend among research papers towards the highest cognitive level of Bloom’s taxonomy – knowledge.

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References

  1. Humphreys, A., Jen-Hui Wang, R.: Automated text analysis for consumer research. J. Consum. Res. 44(6), 1274–1306 (2018)

    Article  Google Scholar 

  2. Justicia De La Torre, C., Sánchez, D., Blanco, I., Martín-Bautista, M.J.: Text mining: techniques, applications, and challenges. Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 26(4), 553–582 (2018)

    Google Scholar 

  3. Taylor, S.J.E., Eldabi, T., Riley, G., Paul, R.J., Pidd, M.: Simulation modelling is 50! Do we need a reality check? J. Oper. Res. Soc. 60(1), 69–82 (2009)

    Article  Google Scholar 

  4. Tocher, K., Owen, D.: The automatic programming of simulation. In: Proceedings of the Second International Conference on Operational Research, pp. 58–60 (1960)

    Google Scholar 

  5. García-García, J.A., Enríquez, J.G., Ruiz, M., Arévalo, C., Jiménez-Ramírez, A.: Software Process Simulation Modeling: Systematic literature review. Comput. Stand. Interfaces 70, 103425 (2020)

    Article  Google Scholar 

  6. Lunesu, M. I., Münch, J., Marchesi, M., Kuhrmann, M.: Using simulation for understanding and reproducing distributed software development processes in the cloud. Inf. Softw. Technol. 103, 226–238 (2018)

    Google Scholar 

  7. Garcia, M.T., Barcelona, M.A., Ruiz, M., Garcia-Borgonon, L.; Ramos, I.: A discrete-event simulation metamodel for obtaining simulation models from business process models. In: José Escalona, M., Aragón, G., Linger, H., Lang, M., Barry, C., Schneider, C. (eds.) Information System Development, pp. 307–317. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07215-9_25

  8. Janses-Vullers, M.H., Netjes, M.: Business Process Simulation - A Tool Survey (2006)

    Google Scholar 

  9. Diallo, S.Y., Gore, R.J., Padilla, J.J., Lynch, C.J.: An overview of modeling and simulation using content analysis. Scientometrics 103(3), 977–1002 (2015)

    Article  Google Scholar 

  10. Frerichs, L., Smith, N.R., Hassmiller, K., Bendor, T.K., Evenson, K.R.: A scoping review of simulation modeling in built environment and physical activity research. Heal. Place 57, 122–130 (2019)

    Google Scholar 

  11. Onggo, B.S.S.: Proceedings of the 2012 Winter Simulation Conference (2012)

    Google Scholar 

  12. Anjomshoae, A., Hassan, A., Rebi, M., Rani, A.: A review of ergonomics and simulation modeling in healthcare delivery system. Adv. Mater. Res. 845, 604–608 (2014)

    Google Scholar 

  13. Fone, D., et al.: Systematic review of the use and value of computer simulation modelling in population health and health care delivery. J Pub. Heal. Med. 25(4), 325–335 (2003)

    Google Scholar 

  14. Salleh, S., Thokala, P., Brennan, A., Hughes, R., Booth, A.: Simulation modelling in healthcare : an umbrella review of systematic literature reviews. PharmacoEconomics 35, 937–949 (2017)

    Google Scholar 

  15. Handel, A., La Gruta, N.L., Thomas, P.G.: Simulation modelling for immunologists. Nat. Rev. Immunol. 20, 186–195 (2019)

    Google Scholar 

  16. Long, K.M., Meadows, G.M.: Simulation modelling in mental health: a systematic review. J. Simul. 12(1), 76–85 (2017)

    Google Scholar 

  17. Moon, Y.B.: Simulation modelling for sustainability: a review of the literature. Int. J. Sustain. Eng. 10(1), 2–19 (2017)

    Article  Google Scholar 

  18. Kirimtat, A., Kundakci, B., Chatzikonstantinou, I., Sariyildiz, S.: Review of simulation modeling for shading devices in buildings. Renew. Sust. Energy Rev. 53, 23–49 (2016)

    Article  Google Scholar 

  19. Cheng, Y., Liu, D., Chen, J., Namilae, S.: Human behavior under emergency and its simulation modeling : a review. In: Cassenti, D. (eds.) Advances in Human Factors in Simulation and Modeling, vol. 780. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94223-0_30

  20. Abdelmegid, M.A., González, V.A., Poshdar, M., Sullivan, M.O., Walker, C.G., Ying, F.: Automation in Construction Barriers to adopting simulation modelling in construction industry. Autom. Constr. 111, 103046 (2020)

    Google Scholar 

  21. Faingloz, L., Tolujew, J.: Simulation modelling application in real-time service systems: review of the literature. Procedia Eng. 178(112), 200–205 (2017)

    Google Scholar 

  22. Zhang, H., Kitchenham, B., Pfahl, D.: Software process simulation modeling: an extended systematic review. In: Münch, J., Yang, Y., Schäfer, W. (eds.) ICSP 2010. LNCS, vol. 6195, pp. 309–320. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14347-2_27

    Chapter  Google Scholar 

  23. Creswell, J.W., Creswell, J.D.: Research Design, 5th edn. SAGE, London (2018)

    Google Scholar 

  24. Ananiadou, S., Procter, R., Thomas, J.: Supporting systematic reviews using text mining. Soc. Sci. Comput. Rev. 27, 509–523 (2009)

    Google Scholar 

  25. Sinoara, R.A., Antunes, J., Rezende, S.O.: Text mining and semantics: a systematic mapping study. J. Braz. Comput. Soc. 23(1), 1–20 (2017). https://doi.org/10.1186/s13173-017-0058-7

    Article  Google Scholar 

  26. Kotu, V., Deshpande, B.: Predictive Analytics and Data Mining. Elsevier, Amsterdam (2015)

    Google Scholar 

  27. Kaur, A., Chopra, D.; Comparison of text mining tools. In: 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), pp. 365–376 (2016)

    Google Scholar 

  28. Zhu, F., Patumcharoenpol, P., Zhang, C., Yang, Y., Chan, J.: Biomedical text mining and its applications in cancer research. J. Biomed. Inform. 46, 200–211 (2013)

    Google Scholar 

  29. Chen, C., Dubin, R., Schultz, T.: Science mapping. In: Encyclopedia of Information Science and Technology, 3rd edn., pp. 4171–4184 (2014)

    Google Scholar 

  30. Chen, C.: Science mapping: a systematic review of the literature. J. Data Inf. Sci. 2(2), 1–40 (2017)

    Google Scholar 

  31. Andres, A.: Measuring Academic Research. Chandos Publishing, Oxford (2009)

    Google Scholar 

  32. Rojas-Sola, J.I., Aguilera-García, A.I.: Global bibliometric analysis of building information modeling through the web of science core collection (2003–2017). Inf. de La Con. 72(557), 1–12 (2020). https://doi.org/10.3989/IC.66768

    Article  Google Scholar 

  33. Teixeira, S., Pocinho, M.: The bibliometric perspective of hotel industry and regional competitiveness: J. Spat. Org. Dyn. 8(2), 129–147 (2020)

    Google Scholar 

  34. Daenekindt, S., Huisman, J.: Mapping the scattered field of research on higher education : a correlated topic model of 17, 000 articles, 1991–2018. High. Educ. 80(516), 1–17 (2020)

    Google Scholar 

  35. RapidMiner: RapidMiner (2020). http://docs.rapidminer.com/

  36. North, M.: Data Mining for the Masses, Global Text Project (2012)

    Google Scholar 

  37. Bayhaqy, A., Sfenrianto, S., Nainggolan, K., Kaburuan, E.: Sentiment analysis about e-commerce from tweets using decision tree, k-nearest neighbor, and Naïve Bayes. In: ICOT 2018 (2019)

    Google Scholar 

  38. Hrcka, L., Simoncicova, V., Tadanai, O., Tanuska, P., Vazan, P.: Using text mining methods for analysis of production data in automotive industry. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. AISC, vol. 573, pp. 393–403. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57261-1_39

    Chapter  Google Scholar 

  39. Van Eck, N.J., Waltman, L.: Text mining and visualization using VOSviewer (2011)

    Google Scholar 

  40. Van Eck, N.J., Waltman, L., Dekker, R., Van Den Berg, J.: A comparison of two techniques for bibliometric mapping: multidimensional scaling and VOS. J. Am. Soc. Inf. Sci. Technol. 61(12), 2405–2416 (2010)

    Google Scholar 

  41. Hallinger, P., Gümüş, S., Bellibaş, M.: ‘Are principals instructional leaders yet?’ A science map of the knowledge base on instructional leadership, 1940–2018. Scientometrics 122, 1629–1650 (2020)

    Google Scholar 

  42. Zurita, G., Shukla, A.K., Pino, J.A., Merigó, J.M., Lobos-Ossandón, V., Muhuri, P.K.: A bibliometric overview of the journal of network and computer applications between 1997 and 2019. J. Network Comput. Appl. 165, 102695 (2020)

    Google Scholar 

  43. Obileke, K., Onyeaka, H., Omoregbe, O., Makaka, G., Nwokolo, N., Mukumba, P.: Bioenergy from bio-waste: a bibliometric analysis of the trend in scientific research from 1998–2018. Bio. Convers. Biorefinery (2020). https://link.springer.com/article/10.1007/s13399-020-00832-9

  44. Silge, J., Robinson, R.: Welcome to Text Mining with R. O’Reilly, Sebastopol (2017)

    Google Scholar 

  45. Arveson, P.: The Deming Cycle. 2020. https://balancedscorecard.org/bsc-basics/articles-videos/the-deming-cycle. Accessed 10 Feb 2020

  46. Bloom, B. S.: Taxonomy of Educational Objectives: The Classification of Educational Goals: Handbook I, Cognitive domain. Longmans: Green, New York (1965)

    Google Scholar 

  47. Britto, R., Usman, M: Bloom’s Taxonomy in Software Engineering Education : A Systematic Mapping Study (2015)

    Google Scholar 

  48. Nentl, N., Zietlow, R.: Using bloom’ s taxonomy to teach critical thinking skills to business students. Coll. Undergrad. Libr. 15(1–2), 159–172 (2008)

    Google Scholar 

  49. Ninčević Pašalić, I., Ćukušić, M., Jadrić, M.: Smart city research advances in Southeast Europe. Int. J. Inf. Manage (2020, in press)

    Google Scholar 

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Acknowledgment

This work has been supported by the Croatian Science Foundation (project No. UIP-2017-05-7625).

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Authors and Affiliations

  1. Faculty of Economics, Business and Tourism, University of Split, Split, Croatia

    Mario Jadrić, Tea Mijač & Maja Ćukušić

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  1. Mario Jadrić
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  2. Tea Mijač
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  3. Maja Ćukušić
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Corresponding author

Correspondence to Tea Mijač .

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Editors and Affiliations

  1. Babeș-Bolyai University, Cluj Napoca, Romania

    Robert Andrei Buchmann

  2. University of Camerino, Camerino, Italy

    Andrea Polini

  3. Linköping University, Linköping, Sweden

    Björn Johansson

  4. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

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Jadrić, M., Mijač, T., Ćukušić, M. (2020). Text Mining the Variety of Trends in the Field of Simulation Modeling Research. In: Buchmann, R.A., Polini, A., Johansson, B., Karagiannis, D. (eds) Perspectives in Business Informatics Research. BIR 2020. Lecture Notes in Business Information Processing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-61140-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-61140-8_10

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