Research on Decision Support Service Model based on Big Data


Ahmed Rizk,Beni Suef University, Egypt


With the rapid development of big data, the potential value of data resources urgently needs to be tapped and utilized. More and more companies choose the form of alliances to realize the complementary advantages of data resources. Through the sharing of data resources of alliance members, it can flexibly support corporate decision-making according to market needs and provide decision-making support services for society more efficiently. Based on the analysis of the research status and related concepts of big data alliances, decision support, service models in various countries, this paper reveals the coupling mechanism of big data alliance decision support services based on the hypercycle theory. On this basis, according to the characteristics of user needs, two decision support service models of big data alliance based on complete user information and incomplete user information are constructed. Decision support service model based on complete user information, including user demand description, information search and matching, service plan formation and delivery and feedback, and interactive and follow-up service methods. Decision support service mode based on incomplete user information, including user demand mining, missing information supplementation, service plan formation, and delivery and feedback, and detailed consultation and multiple negotiation service methods. The Big Data Alliance focuses on promoting cooperation among various industries in the process of providing decision support services. Vigorously promote the application of big data and the technological achievements of the big data alliance in various industries, and realize the complementation of data resources in different industries and multiple dimensions. Promote the overall development level of member companies in the Big Data Alliance.



Big data, Decision support, User information, Service model


[1] A. Marchand, A. Donald, and J. Peppard, “Why IT fumbles analytics,” Harvard Business Review, (2013), vol.91, no1-2, pp.104-108
[2] J. Akoka, W. I. Comyn, and N. Laoufi, “Research on big data: A systematic mapping study,” Computer Standards and Interfaces, (2017), vol.54, no.89, pp.105-115
[3] A. R. Ali, I. A. Zualkernan, and M. Rashid, “A smart home energy management system using IoT and big data analytics approach,” Transactions on Consumer Electronics, (2018), vol.63, no.4, pp.426-434
[4] E. Correa, E. Inga, and J. Inga, “Electrical consumption pattern base on meter data management system using big data techniques,” International Conference on Information Systems and Computer Science, (2018), pp.334-339
[5] E. Mckayand M. D. Mohamad, “Correction to big data management skills: Accurate measurement,” Research and Practice in Technology Enhanced Learning, (2018), vol.13, no.1, pp.8-9
[6] N. Golov and L. Ronnback, “Big data normalization for massively parallel processing databases,” Computer Standards and Interfaces, (2017), vol,54, no.3, pp.86-93
[7] C. Marco and P. Anit, “How organizations leverage big data: A maturity model, Industrial Management and Data Systems, (2016), vol.116, no.8, pp.1468-1492
[8] G. Manco, E. Ritacco, and P. Rullo, “Fault detection and explanation through big data analysis on sensor streams,” Expert Systems with Applications, (2017), vol.87, no.19, pp.141-156
[9] M. I. Pramanika, R. Y. Lau, and H. Demirkan, “Smart health: Big data-enabled health paradigm within smart cities,” (2017), vol.32, no.2, pp.1-11
[10] J. S. Johnson, S. B. Friend, and H. S. Lee, “Big data facilitation, utilization, and monetization: Exploring the 3vs in a new product development process,” Journal of Product Innovation Management, (2017), vol.34, no.5, pp.23-32
[11] O. E. Nils and B. Heidi, “Use of big data in project evaluations,” International Journal of Managing Projects in Business, (2015), vol.8, no.3, pp.491-512
[12] S. Chahine, K. M. Kulasegaram, and S. Wright, “A call to investigate the relationship between education and health outcomes using big data,” Academic Medicine Journal of the Association of American Medical Colleges, (2018), vol.93, no.6, pp.1-2
[13] K. S. Fontaine, “The research data alliance,” AGU Fall Meeting. AGU Fall Meeting Abstracts, (2015), pp.112-116
[14] A. Treloar, “The research data alliance: Globally coordinated action against barriers to data publishing and sharing,” Learned Publishing, (2016), vol.27, no.5, pp.9-13
[15] A. P. Empster, “Upper and lower probabilities induced by a multivalued mapping,” The Annals of Mathematical Statistics, vol.38, no.2, pp.325-339
[16] G. C. Rota, “A mathematical theory of evidence,” G. Shafer, Princeton University Press, Advances in Mathematics, vol,24, no.3, pp.341-341
[17] Z. Hua, B. Gong, and X. Xu, “A DS–AHP approach for multi-attribute decision making problem with incomplete information,” Expert Systems with Applications, (2008), vol,34, no.3, pp.2221-2227
[18] S. Y. Hwang and W. S. Yang, “On the discovery of process models from their instances,” Decision Support Systems, (2003), vol,34, no.1, pp.41-57
[19] M. C. Rousset and C. Reynaud, “Knowledge representation for information integration,” Information Systems, (2004), vol.29, no.1, pp.3-22
[20] A. G. Pateli, “Decision making on the governance of strategic technology alliance,” Management Decision, (2009), vol.47, no.2, pp.246 -270


  • APA:
    Rizk,A.(2018). Research on Decision Support Service Model Based on Big Data. International Journal of Smart Business and Technology, 6(2), 39-52. 10.21742/IJSBT.2018.6.2.05
  • Harvard:
    Rizk,A.(2018). "Research on Decision Support Service Model Based on Big Data". International Journal of Smart Business and Technology, 6(2), pp.39-52. doi:10.21742/IJSBT.2018.6.2.05
  • IEEE:
    [1] A.Rizk, "Research on Decision Support Service Model Based on Big Data". International Journal of Smart Business and Technology, vol.6, no.2, pp.39-52, Dec. 2018
  • MLA:
    Rizk Ahmed. "Research on Decision Support Service Model Based on Big Data". International Journal of Smart Business and Technology, vol.6, no.2, Dec. 2018, pp.39-52, doi:10.21742/IJSBT.2018.6.2.05


  • Volume 6, No. 2, 2018
  • ISSN(p):2288-8969
  • ISSN(e):2207-516X
  • Published:Dec. 2018