Abstract
“The work of an information scientist includes three kinds of tasks: the first one is structuring, the second one is structuring, and the third one is structuring” (Becker, Jörg, often). This wise statement that we have learned from Jörg Becker tells us that we can solve nearly any problem related to information systems through transforming it into a structured form—be it a conceptual model, a formula or an algorithm. Another maxim that we share with Jörg Becker is that it is very important to go skiing—both for leisure and for academia. It is obvious that we should bring both maxims together. Structuring skiing? How does that work? Well, we can do this by discovering the structure of ski resorts (first structuring), learn about the structure of typical routes of skiers (second “structuring”) and by providing structured statements on the future movement behaviour of skiers (third “structuring”). But why should we do this? In this article, we show you why, how it works and how we can benefit from it.
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References
Becker, J., Breuker, D., Delfmann, P., & Matzner, M. (2014). Designing and implementing a framework for event-based predictive modelling of business processes. In Enterprise Modelling and Information Systems Architectures—EMISA 2014 (pp. 71–84).
Breuker, D., Delfmann, P., Matzner, M., & Becker, J. (2015). Designing and evaluating an interpretable predictive modeling technique for business processes. In Business Process Management Workshops (pp. 202, 541–553). https://doi.org/10.1007/978-3-319-15895-2_46.
Breuker, D., Matzner, M., Delfmann, P., & Becker, J. (2016). Comprehensible predictive models for business processes. MISQ, 40(4), 1009–1034.
Brunk, J., Riehle, D. M., & Delfmann, P. (2018). Prediction of customer movements in large tourism industries by the means of process mining. In European Conference on Information Systems. Portsmouth, UK.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B Methodological, 39(1), 1–38.
Domaines Skiables de France. (2017). Indicateurs et analyses 2017.
Pinch, R. (2016). Maximum-likelihood method. In Encyclopedia of Mathematics (p. 1). Retrieved from http://www.encyclopediaofmath.org/index.php?title=Maximum-likelihood_method&oldid=13827.
Roth, R.-D., Krämer, A., & Görtz, M. (2012). Grundlagenstudie Wintersport Deutschland.
Seilbahnen Schweiz. (2017). Fakten und Zahlen zur Schweizer Seilbahnenbranche.
Vidal, E., Thollard, F., & de la Higuera, C. (2005). Probabilistic finite-state machines-part I. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7), 1013–1025.
Walton, C. A. (1983). Portable radio frequency emitting identifier. US Patent (4, 384, 288). Retrieved from https://patents.google.com/patent/US4384288.
Wirtschaftskammer Österreich. (2017). Zahlen/Daten/Fakten. Retrieved from https://www.wko.at/branchen/transport-verkehr/seilbahnen/ZahlenDatenFakten.html.
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Delfmann, P. (2019). Predictive Analytics of Winter Sports Processes Using Probabilistic Finite Automata. In: Bergener, K., Räckers, M., Stein, A. (eds) The Art of Structuring. Springer, Cham. https://doi.org/10.1007/978-3-030-06234-7_17
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DOI: https://doi.org/10.1007/978-3-030-06234-7_17
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