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Big Data Framework for Analytics Business Intelligence

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 551)


Business intelligence (BI) systems collect, store, analyze, and present data to help businesses make better decisions. In today’s extremely competitive and difficult industry, it is critical to develop and operate BI systems. Business intelligence (BI) approaches are now widely used in many industries that rely on decision-making. To obtain, evaluate, and anticipate business-critical information, business intelligence (BI) is used. Traditionally, business intelligence seeks to collect, retrieve, and categorize data to handle requests efficiently and effectively. The Internet of things (IoT), the advent of big data, cloud computing, and artificial intelligence (AI) have all increased the importance of business intelligence (BI). Many issues arise in strategic decision-making. The main goal of this research is to get past these problems and build a technical foundation for big data analytics and business intelligence.


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  1. Yafooz WMS, Abidin SZ, Omar N (2011) Challenges and issues on online news management. In: 2011 IEEE international conference on control system, computing and engineering (ICCSCE). IEEE, pp 482–487

    Google Scholar 

  2. Unstructured data: the hidden threat in digital business. (2021). TechNative.

  3. Balachandran BM, Prasad S (2017) Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Comput Sci 112:1112–1122

    Article  Google Scholar 

  4. Kimble C, Milolidakis G (2015) Big data and business intelligence: debunking the myths. Glob Bus Organ Excell 35:23–34

    Article  Google Scholar 

  5. Richards G, Yeoh W, Chong AYL, Popovic A (2017) Business intelligence effectiveness and corporate performance management an empirical analysis. J Comput Inf Syst:1–9

    Google Scholar 

  6. Xia BS, Gong (2014) Review of business intelligence through data analysis. Benchmarking Int J 21:300–311

    Google Scholar 

  7. Kowalczyk M, Buxmann P (2014) Big data and information processing in organizational decision processes: a multiple case study. Bus Inf Syst Eng 5(2014):267–278

    Article  Google Scholar 

  8. Ştefanescu A, Ştefanescu L, Ciora IL (2009) Intelligent tools and techniques for modern management. Chin Bus Rev 8(2):46–54

    Google Scholar 

  9. Gartner (2014) Worldwide business intelligence and analytics software market grew 8% in 2013.Retreived from

  10. Markarian J, Brobst S, Bedell J (2007) Critical success factors deploying pervasive BI. Informatica Teradata MicroStrategy:1–18

    Google Scholar 

  11. Dagan B (2007) Dashboards and scorecards aid in performance management and monitoring. Nat Gas Electricity 24(2):23–27

    Google Scholar 

  12. White C (2006) The next generation of business intelligence: operational BI. Retrieved from, 16

  13. Gad-Elrab Ahmed AA (2021) Modern business intelligence: big data analytics and artificial intelligence for creating the data-driven value. IntechOpen.

  14. Constantiou ID, Kallinikos J (2015) New games, new rules: big data and the changing context of strategy. J Inf Technol 30:44–57

    Article  Google Scholar 

  15. Oussous A, Benjelloun FZ, Lahcen AA, Belfkih S (2018) Big data technologies: a survey. J King Saud Univ Comput Inf Sci:431–448

    Google Scholar 

  16. Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data J:51–59

    Google Scholar 

  17. Elgendy N, Elragal A (2014) Big data analytics: a literature review paper. s. l. Springer, cham, pp 214–227

    Google Scholar 

  18. Elragal A, Klischewski R (2017) Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. J Big Data:19

    Google Scholar 

  19. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35:137–144

    Article  Google Scholar 

  20. White T (2012) Hadoop: the definitive guide, 3rd edn. O’Reilly Media Inc., Sebastopol, CA, USA

    Google Scholar 

  21. Apache Hive. Available online: Accessed 28 July 2021

  22. Apache Pig. Available online: Accessed 28 July 2021

  23. Apache Flume. Available online: Accessed 28 July 2021

  24. Khoshbakht F (2021) Role of the big data analytic framework in business intelligence and its impact: need and benefits. Turk J Comput Math Educ (TURCOMAT) 12(10):560–566

    Google Scholar 

  25. Khoshbakht F, Shiranzaei A, Quadri SMK (2020) Adoption of big data analytics framework for business intelligence and its effectiveness: an analysis. PalArch’s J Archaeol Egypt/Egyptology 17(9):4776–4791

    Google Scholar 

  26. Khoshbakht F, Shiranzaei A, Quadri SMK (2020) A Technological performance analysis of big data analytics framework for business intelligence. Solid State Technol 63(6):19701–19713

    Google Scholar 

  27. Khoshbakht F, Quadri SMK, A study on analytics of big data and business intelligence-a review

    Google Scholar 

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Correspondence to Farhad Khoshbakht .

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Khoshbakht, F., Quadri, S.M.K. (2023). Big Data Framework for Analytics Business Intelligence. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 551. Springer, Singapore.

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