Information Systems Frontiers

, Volume 21, Issue 1, pp 27–44 | Cite as

Towards a Reuse Strategic Decision Pattern Framework – from Theories to Practices

  • Victor ChangEmail author
  • Mohamed Abdel-Basset
  • Muthu Ramachandran


This paper demonstrates our proposed Reuse Strategic Decision Pattern Framework (RSDPF) based on blending ANP and TOPSIS techniques, enabled by the OSM model with data analytics. The motivation, related work, theory, the use and deployment, and the service deployment of the framework have been discussed in details. In this paper, RSDPF framework is demonstrated by the data analysis and interpretations based on a financial service firm. The OSM model allows 3 step of processed to be performed in one go to perform statistical tests, identify linear relations, check consistency on dataset and calculate OLS regression. The aim is to identify the actual, expected and risk rates of profitability. Code and services can be reused to compute for analysis. Service integration of the RSDPF framework has been demonstrated. Results confirm that there is a high extent of reliability. In this paper, we have demonstrated the reuse and integration of the framework supported by the case study of the financial service firm with its data analysis and service to justify our research contributions – reuse and integration in statistical data mining, knowledge and heuristic discovery and finally domain transference.


Reuse and integration RSDPF framework Predictive analytics pattern ANP and TOPSIS techniques OSM case study Service integration for data science 


  1. Abrahamsson, P., Salo, O., Ronkainen, J., & Warsta, J. (2017). Agile software development methods: Review and analysis. arXiv preprint arXiv:1709.08439.Google Scholar
  2. Adalı, E. A., & Işık, A. T. (2017). The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem. Journal of Industrial Engineering International, 13(2), 229–237.CrossRefGoogle Scholar
  3. Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., & Herrera, F. (2011). Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic & Soft Computing, 17, 255–287.Google Scholar
  4. Barga, R., Fontama, V, & Tok, W. Y (2014) Predictive analytics with Microsoft azure machine learning: Build and deploy actionable solutions in minutes, Apress/springer, ISBN 978–1–4842-0446-7.Google Scholar
  5. Bellifemine, F., Caire, G., Poggi, A., & Rimassa, G. (2008). JADE: A software framework for developing multi-agent applications. Lessons learned. Information and Software Technology, 50(1), 10–21.CrossRefGoogle Scholar
  6. Boehm, B. (2006, May). A view of 20th and 21st century software engineering. In Proceedings of the 28th international conference on Software engineering (pp. 12–29). ACM.Google Scholar
  7. Bonaccorsi, A., & Rossi, C. (2003). Why open source software can succeed. Research Policy, 32(7), 1243–1258.CrossRefGoogle Scholar
  8. Bruch, M., Mezini, M., & Monperrus, M. (2010, May). Mining subclassing directives to improve framework reuse. In Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on (pp. 141–150). IEEE.Google Scholar
  9. Chang, V. (2014). A proposed model to analyse risk and return for cloud adoption. Lambert Academic Publishing, ISBN: 978-3-659-58769-6.Google Scholar
  10. Chang, V. (2017). Presenting cloud business performance for manufacturing organizations. Information Systems Frontiers, 1–17.Google Scholar
  11. Chang, V., & Wills, G. (2016). A model to compare cloud and non-cloud storage of big data. Future Generation Computer Systems, 57, 56–76.CrossRefGoogle Scholar
  12. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.CrossRefGoogle Scholar
  13. Cockburn, A. (2006). Agile software development: the cooperative game. Pearson Education,2nd Edtion, ISBN 0321482751.Google Scholar
  14. Cordell, D., Rosemarin, A., Schröder, J. J., & Smit, A. L. (2011). Towards global phosphorus security: A systems framework for phosphorus recovery and reuse options. Chemosphere, 84(6), 747–758.CrossRefGoogle Scholar
  15. Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4(1), 50.CrossRefGoogle Scholar
  16. Engwall, M. (2003). No project is an island: Linking projects to history and context. Research Policy, 32(5), 789–808.CrossRefGoogle Scholar
  17. Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier.Google Scholar
  18. Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation (adobe reader). Pearson Education.Google Scholar
  19. Jennings, N. R. (2001). An agent-based approach for building complex software systems. Communications of the ACM, 44(4), 35–41.CrossRefGoogle Scholar
  20. Khan, W. Z., Xiang, Y., Aalsalem, M. Y., & Arshad, Q. (2013). Mobile phone sensing systems: A survey. IEEE Communications Surveys & Tutorials, 15(1), 402–427.CrossRefGoogle Scholar
  21. Kirk, D., Roper, M., & Wood, M. (2007). Identifying and addressing problems in object-oriented framework reuse. Empirical software engineering, 12(3), 243–274.CrossRefGoogle Scholar
  22. Ko, A. J., Abraham, R., Beckwith, L., Blackwell, A., Burnett, M., Erwig, M., et al. (2011). The state of the art in end-user software engineering. ACM Computing Surveys (CSUR), 43(3), 21.CrossRefGoogle Scholar
  23. Lee, S., Kang, Y., Ialongo, N. S., & Prabhu, V. V. (2016). Predictive analytics for delivering prevention services. Expert Systems with Applications, 55, 469–479.CrossRefGoogle Scholar
  24. Leung, C. K., Jiang, F., Zhang, H., & Pazdor, A. G. (2016, August). A Data Science Model for Big Data Analytics of Frequent Patterns. In Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2016 I.E. 14th Intl C (pp. 866–873). IEEE.Google Scholar
  25. Mather, T., Kumaraswamy, S., & Latif, S. (2009). Cloud security and privacy: an enterprise perspective on risks and compliance. " O'Reilly Media, Inc.".Google Scholar
  26. Papazoglou, M. P., & Heuvel, W. J. (2007). Service oriented architectures: Approaches, technologies and research issues. The VLDB Journal—The International Journal on Very Large Data Bases, 16(3), 389–415.CrossRefGoogle Scholar
  27. Papazoglou, M. P., Traverso, P., Dustdar, S., & Leymann, F. (2008). Service-oriented computing: A research roadmap. International Journal of Cooperative Information Systems, 17(02), 223–255.CrossRefGoogle Scholar
  28. Patton, W., & McMahon, M. (2006). The systems theory framework of career development and counseling: Connecting theory and practice. International Journal for the Advancement of Counselling, 28(2), 153–166.CrossRefGoogle Scholar
  29. Pressman, R. S. (2005). Software engineering: A practitioner's approach (6th Edition, ISBN ed.). Palgrave Macmillan. isbn:0-07-285318-2.Google Scholar
  30. Ramachandran, M. (2008). Software components: Guidelines and applications. NY: Nova science.Google Scholar
  31. Ramachandran, M and Jamnal, G (2014) Developing reusable. NET software components, Science and Information Conference (SAI), 2014.Google Scholar
  32. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.Google Scholar
  33. Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS publications.Google Scholar
  34. Saaty T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841–855.Google Scholar
  35. Saaty, T. L. (2004). Decision making—The analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13, 1–35.CrossRefGoogle Scholar
  36. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge university press.Google Scholar
  37. Sun, G., Chang, V., Yang, G., & Liao, D. (2018). The cost-efficient deployment of replica servers in virtual content distribution networks for data fusion. Information Sciences, 432, 495–515.CrossRefGoogle Scholar
  38. Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: Methods and applications. CRC press.Google Scholar
  39. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.Google Scholar
  40. Xin, T., & Yang, L. (2017, June). A framework of software reusing engineering management. In Software Engineering Research, Management and Applications (SERA), 2017 I.E. 15th International Conference on (pp. 277–282). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Victor Chang
    • 1
    • 2
    Email author
  • Mohamed Abdel-Basset
    • 3
  • Muthu Ramachandran
    • 4
  1. 1.International Business School SuzhouXi’an Jiaotong-Liverpool UniversitySuzhouChina
  2. 2.Research Institute of Big Data AnalyticsXi’an Jiaotong-Liverpool UniversitySuzhouChina
  3. 3.Department of Operations Research, Faculty of Computers and InformaticsZagazig UniversitySharqiyahEgypt
  4. 4.Leeds Beckett UniversityLeedsUK

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