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  4. Toward new data for IT and IoT project management method prediction

Toward new data for IT and IoT project management method prediction

MMC.
2023;
Volume 10, Number 2
: pp. 557–565
https://doi.org/10.23939/mmc2023.02.557
Received: February 13, 2023
Accepted: April 21, 2023

Mathematical Modeling and Computing, Vol. 10, No. 2, pp. 557–565 (2023)

Authors:
  1. S. Merzouk
  2. R. Gandoul
  3. A. Marzak
  4. N. Sael
1
Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
2
Laboratory of Sciences and Technology of Information and Education, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
3
Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca
4
Laboratory of Information Technology and Modeling, Faculty of sciences Ben M'Sik, Hassan II University of Casablanca

Selecting the best project management method at the workplace helps to deliver a high-quality product to the customer.  Hence, the need for good knowledge of management methods, their characteristics, advantages, and disadvantages, is necessary to be able to select the best for the specific project.  However, until now, no large dataset for Machine Learning and decision-making model, model or system has been proposed to help project managers to the most efficient method adapted to the constraints of their projects.  This work develops the construction of the dataset for agile and IoT project management method based on the real experiences.  In this paper, our objective is to propose a criteria-based model that allows the choice of the best management method to adopt for such an IT or IoT project according to a set of criteria.

agile method
information technology (IT)
internet of things (IoT)
project management
dynamic system development method (DSDM)
  1. Patil M., Suresh M.  Modelling the enablers of workforce agility in IoT projects: a TISM approach.  Global Journal of Flexible Systems Management.  20 (2), 157–175 (2019).
  2. Hurtoi V., Avadanei D.  IoT Project Management.  Informatica Economică.  24 (3), 75–80 (2020).
  3. Budiman R.  Utilizing Skype for Providing Learning Support for Indonesian Distance Learning Students: A Lesson Learnt.  Procedia – Social and Behavioral Sciences.  83, 5–10 (2013).
  4. Schwaber K., Sutherland J.  The Scrum Guide The Definitive Guide to Scrum: The Rules of the Game.  https://scrumguides.org/scrum-guide.html.
  5. Dudziak T.  Extreme programming an overview.  Methoden Werkzeuge Softwareproduktion WS. 1–28 (1999/2000).
  6. What is SAFe$^\circledR$? Scaled Agile', SAFe$^\circledR$ Enterprise Solutions.  https://www.scaledagile.com/enterprise-solutions/what-is-safe/.
  7. Slama D., Puhlmann F., Morrish J., Bhatnagar R. M.  Eds., Enterprise IoT: strategies and best practices for connected products and services.  Beijing Boston Farnham Sebastopol Tokyo: O'Reilly (2016).
  8. IoT Methodology – The Internet of Things project lifecycle guide for creative, technical and business people.  http://www.iotmethodology.com/.
  9. Merzouk S., Cherkaouii A., Marzak A., Nawal S.  IoT methodologies: comparative study.  Procedia Computer Science.  175, 585–590 (2020).
  10. Merzouk S., Elhadi S., Ennaji H., Marzak A., Nawal S.  A Comparative Study of Agile Methods: Towards a New Model-based Method.  International Journal of Web Applications.  9 (4), 121–128 (2017).
  11. Manifesto for Agile Software Development.  https://agilemanifesto.org/.  Manifesto for Agile Software Development (2001).
  12. Dynamic Systems Development Method (1998).  https://docplayer.fr/5488317-Dynamic-systems-development-method.html.
  13. Pouyandeh F., Golabchi M., Taghizadeh K.  Providing a model to choose the most appropriate agile method in construction projects.  Proceedings of the Institution of Civil Engineers – Management, Procurement and Law.   176 (1), 14–27 (2022).
  14. Software Development Methods.  https://www.kaggle.com/datasets/mostafizmim/software-development-methods.
  15. Burkov A.  The Hundred-Page Machine Learning Book. Polen (2019).
  16. Edgar T. W., Manz D. O.  Chapter 6 – Machine Learning.  Research Methods for Cyber Security. 153–173 (2017).
  17. Mahesh B.  Machine learning algorithms – a review.  International Journal of Science and Research.  9 (1), 381–386 (2020).
  18. Sulova S.  Association rule mining for improvement of IT project management.  TEM Journal.  7 (4), 717–722 (2018).
  19. Barik S., Mishra D., Mishra S., Satapathy S. K., Rath A. K., Acharya M.  Pattern discovery using fuzzy FP-growth algorithm from gene expression data.  International Journal of Advanced Computer Science and Applications.  1 (5), (2010).