This paper
offers preliminary work on system dynamics and Data mining tools. It tries to understand
the dynamics of carrying out large-scale events, such as Hajj. The study looks at
a large, recurring problem as a variable to consider, such as how the flow of people
changes over time as well as how location interacts with placement. The predicted
data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and
AnyLogic 7.3.1 software regarding the potential placement of temporal service points,
taking into consideration the three dynamic constraints and behavioral aspects:
a large population, limitation in time, and space. This research proposes appropriate
data analyses to ensure the optimal positioning of the service points with limited
time and space for large-scale events. The conceptual framework would be the output
of this study. Knowledge may be added to the insights based on the technique.
References
[1]
Felemban, E.A., Rehman, F.U., Biabani, S.A.A., Ahmad, A., Naseer, A., Majid, A.R., et al. (2020) Digital Revolution for Hajj Crowd Management: A Technology Survey. IEEE Access, 8, 208583-208609. https://doi.org/10.1109/ACCESS.2020.3037396
[2]
Mohammed, R.Y. (2021) Optimizing Temporal Business Opportunities. International Journal of Business and Management, 15, 104-110. https://doi.org/10.5539/ijbm.v15n11p104
[3]
Hoye, R., Smith, A.C., Nicholson, M. and Stewart, B. (2018) Sport Management: Principles and Applications. Routledge, London. https://doi.org/10.4324/9781351202190
[4]
Beis, D.A., Loucopoulos, P., Pyrgiotis, Y. and Zografos, K.G. (2006) PLATO Helps Athens Win Gold: Olympic Games Knowledge Modeling for Organizational Change and Resource Management. Interfaces, 36, 26-42. https://doi.org/10.1287/inte.1060.0189
[5]
Loucopoulos, P., Zografos, K. and Prekas, N. (2003) Requirements Elicitation for the Design of Venue Operations for the Athens 2004 Olympic Games. Proceedings 11th IEEE International Requirements Engineering Conference, Monterey Bay, 12 September 2003, 223-232. https://doi.org/10.1109/ICRE.2003.1232753
[6]
Kwakkel, J.H. and Pruyt, E. (2015) Using System Dynamics for Grand Challenges: The ESDMA Approach. Systems Research and Behavioral Science, 32, 358-375. https://doi.org/10.1002/sres.2225
[7]
Wijermans, N., Conrado, C., van Steen, M., Martella, C. and Li, J. (2016) A Landscape of Crowd-Management Support: An Integrative Approach. Safety Science, 86, 142-164. https://doi.org/10.1016/j.ssci.2016.02.027
[8]
Mikut, R. and Reischl, M. (2011) Data Mining Tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1, 431-443. https://doi.org/10.1002/widm.24
[9]
Perumal, M., Velumani, B., Sadhasivam, A. and Ramaswamy, K. (2015) Spatial Data Mining Approaches for GIS—A Brief Review. In: Satapathy, S., Govardhan, A., Raju, K. and Mandal, J., Eds., Emerging ICT for Bridging the Future—Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2, Springer, Cham, 579-592. https://doi.org/10.1007/978-3-319-13731-5_63
[10]
Vilela, J., Castro, J., Martins, L.E.G. and Gorschek, T. (2017) Integration between Requirements Engineering and Safety Analysis: A Systematic Literature Review. Journal of Systems and Software, 125, 68-92. https://doi.org/10.1016/j.jss.2016.11.031
[11]
Sharma, D., Bhondekar, A.P., Shukla, A.K. and Ghanshyam, C. (2018) A Review on Technological Advancements in Crowd Management. Journal of Ambient Intelligence and Humanized Computing, 9, 485-495. https://doi.org/10.1007/s12652-016-0432-x
[12]
Sterman, J.D. (2001) System Dynamics Modeling: Tools for Learning in a Complex World. California Management Review, 43, 8-25. https://doi.org/10.2307/41166098
[13]
Bakasa, C. (2022) Modelling the Environmental, Social, and Economic Implications of Using Fruit Pomace as an Alternative Livestock Feed Resource: A System Dynamic Modelling Approach. Master’s Thesis, Stellenbosch University, Stellenbosch.
[14]
Wu, X., Zhu, X., Wu, G.Q. and Ding, W. (2014) Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26, 97-107. https://doi.org/10.1109/TKDE.2013.109
[15]
Chen, P.C., Dokic, T. and Kezunovic, M. (2014) The Use of Big Data for Outage Management in Distribution Systems. International Conference on Electricity Distribution (CIRED) Workshop, Rome, 11-12 June 2014, 1-5.
[16]
George, S. and Santra, A.K. (2020) Traffic Prediction Using Multifaceted Techniques: A Survey. Wireless Personal Communications, 115, 1047-1106. https://doi.org/10.1007/s11277-020-07612-8
[17]
Burstedde, C., Klauck, K., Schadschneider, A. and Zittartz, J. (2001) Simulation of Pedestrian Dynamics Using a Two-Dimensional Cellular Automaton. Physica A: Statistical Mechanics and Its Applications, 295, 507-525. https://doi.org/10.1016/S0378-4371(01)00141-8
[18]
Still, G.K. (2000) Crowd Dynamics. Master’s Thesis, University of Warwick, Coventry.
[19]
Cuzzocrea, A., Leung, C.K.S. and MacKinnon, R.K. (2014) Mining Constrained Frequent Itemsets from Distributed Uncertain Data. Future Generation Computer Systems, 37, 117-126. https://doi.org/10.1016/j.future.2013.10.026
[20]
Heppenstall, A.J., Harland, K., Ross, A.N. and Olner, D. (2013) Simulating Spatial Dynamics and Processes in a Retail Gasoline Market: An Agent-Based Modeling Approach. Transactions in GIS, 17, 661-682. https://doi.org/10.1111/tgis.12027
[21]
van der Knaap, W.G. (1999) GIS-Oriented Analysis of Tourist Time-Space Patterns to Support Sustainable Tourism Development. Tourism Geographies, 1, 56-69. https://doi.org/10.1080/14616689908721294
[22]
Borshchev, A. (2013) Multi-method modelling: AnyLogic. In: Brailsford, S., Churilov, L. and Dangerfield, B. Eds., Discrete-Event Simulation and System Dynamics for Management Decision Making, John Wiley & Sons, New York, 284-279. https://doi.org/10.1002/9781118762745.ch12
[23]
Koshak, N. (2005) A GIS-Based Spatial-Temporal Visualization of Pedestrian Groups Movement to and from Jamart Area. Proceedings of Computers in Urban Planning and Urban Management (CUPUM ’05) Conference, 29 June - 1 July 2005, London, 1-10.
[24]
Han, J., Lakshmanan, L.V. and Ng, R.T. (1999) Constraint-Based, Multidimensional Data Mining. Computer, 32, 46-50. https://doi.org/10.1109/2.781634
[25]
Miller, H.J. and Han, J. (2001) Geographic Data Mining and Knowledge Discovery: An Overview. CRC Press, London. https://doi.org/10.4324/9780203468029_chapter_1
[26]
Fayoumi, A. and Loucopoulos, P. (2014) Business Rules, Constraints and simuLation for Enterprise Governance. In: Barjis, J. and Pergl, R., Eds., EOMAS 2014: Enterprise and Organizational Modeling and Simulation, Springer, Berlin, 96-112. https://doi.org/10.1007/978-3-662-44860-1_6
[27]
Cassandras, C.G. and Lafortune, S. (2009) Introduction to Discrete Event Systems. Springer, Berlin. https://doi.org/10.1007/978-0-387-68612-7
[28]
Pruyt, E. (2013) Small System Dynamics Models for Big Issues: Triple Jump towards Real-World Complexity. TU Delft Library, Delft.
[29]
Borshchev, A. and Filippov, A. (2004) From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools. The 22nd International Conference of the System Dynamics Society, Oxford, 25-29 July 2004, 25-29.
[30]
Drezner, T., Drezner, Z. and Salhi, S. (2006) A Multi-Objective Heuristic Approach for the Casualty Collection Points Location Problem. Journal of the Operational Research Society, 57, 727-734. https://doi.org/10.1057/palgrave.jors.2602047
[31]
Malczewski, J. and Rinner, C. (2015) Multicriteria Decision Analysis in Geographic Information Science. Springer, New York. https://doi.org/10.1007/978-3-540-74757-4