Research Challenges in Big Data Solutions in Different Applications

  • Bhawna DhupiaEmail author
  • M. Usha Rani
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Data is the most important unit of information. Now a day, data are being generated in a phenomenal speed. Data is being collected from various sources like social media, sensors, machines, etc. To get vital information, it is very important that the data should get processed in very smart and intelligent way. Traditional approach of processing data is not capable of processing the humongous data generated these days. So to overcome the problem of smart processing of data, Big Data analytics came into existence. Many scientists are working to make it more efficient. This technique is using the latest ways to process the data generated from various sources. It’s just not only store and process the data, but keep the integrity of the data also, as some data are very confident for the organizations. If some organization is sharing their data, their primary requirement is the confidentiality and integrity of the data. Big Data analytics take care of the requirement of the organization. It has been proven a very powerful method for processing of data in the area of surveillance, health care, fraud detection, reduction of crime, etc. The purpose of this paper is to discuss the features of Big Data and its applications. In this paper, the state of the art and applications of Big Data will be discussed. We hashed out about the work already done in the area of improving the integrity and usability of data generated by using Big Data analytics techniques. This will also cover the latest solutions offered by the researchers for the challenges in Big Data analytics.


Big data analytics Applications of big data Research challenges 


  1. 1.
    Jagadish HV et al (2014) Big data and its technical challenges. Commun ACM 57(7):86–94CrossRefGoogle Scholar
  2. 2.
    Rodríguez-Mazahua L, Rodríguez-Enríquez C-A, Sánchez-Cervantes JL, Cervantes J, García-Alcaraz JL, Alor-Hernández G (2016) A general perspective of big data: applications, tools, challenges and trends. J Supercomputing 72(8):3073–3113CrossRefGoogle Scholar
  3. 3.
    Lomotey RK, Deters R (2014) Towards knowledge discovery in big data. In: Proceeding of the 8th international symposium on service oriented system engineering. IEEE Computer Society, pp 181–191Google Scholar
  4. 4.
    Candela L, Castelli D, Pagano P (2012) Managing big data through hybrid data infrastructures. ERCIM News 89:37–38Google Scholar
  5. 5.
    Patel, JA, Sharma P (2014) Big data for better health planning. In: 2014 international conference on advances in engineering and technology research (ICAETR), pp 1–5. IEEEGoogle Scholar
  6. 6.
    Veenadhari S, Misra B, Singh CD (2011) Data mining techniques for predicting crop productivity—a review article. In: IJCST 2(1)Google Scholar
  7. 7.
    Alberto G-S, Juan F-S, Ojeda-Bustamante W (2014) Predictive ability of machine learning methods for massive crop yield prediction Span. J Agric Res 12(2):313–328Google Scholar
  8. 8.
    Gleaso CP (1982) Large area yield estimation/forecasting using plant process models. Paper presentation at the winter meeting American society of agricultural engineers palmer house, Chicago, Illinois, 14–17Google Scholar
  9. 9.
    Khan, S, Shakil KA, Alam M (2016) Educational intelligence: applying cloud-based big data analytics to the Indian education sector. In: 2016 2nd international conference on contemporary computing and informatics (IC3I), pp 29–34. IEEEGoogle Scholar
  10. 10.
    Smart Cities—Make In India., 2016. [Online]. Available:
  11. 11.
    Education sector in India, Indian education system, Industry. N.p., 2016. Web. 8 June 2016Google Scholar
  12. 12.
    Sin, K, Muthu L (2015) Application of big data in education data mining and learning analytics—a literature review. ICTACT J Soft Comput 5(4)Google Scholar
  13. 13.
    Blikstein P (2011) Using learning analytics to assess students’ behavior in open-ended programming tasks. In: Proceedings of the 1st international conference on learning analytics and knowledge, pp 110–116. ACMGoogle Scholar
  14. 14.
    Brown DE (1998) The regional crime analysis program (RECAP): a framework for mining data to catch criminals. In: 1998 IEEE international conference on systems, man, and cybernetics, 1998, vol 3, pp 2848–2853. IEEEGoogle Scholar
  15. 15.
    Xu JJ, Chen H (2005) CrimeNet explorer: a framework for criminal network knowledge discovery. ACM Trans Inf Syst (TOIS) 23(2):201–226MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sparrow MK (1991) The application of network analysis to criminal intelligence: an assessment of the prospects. Soc Netw 13(3):251–274CrossRefGoogle Scholar
  17. 17.
    Malleson N, Andresen MA (2015) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography Geogr Inf Sci 42(2):112–121CrossRefGoogle Scholar
  18. 18.
    Mena, J (2003) Investigative data mining for security and criminal detection. Butterworth-HeinemannGoogle Scholar
  19. 19.
    Kitchin R (2014) The real-time city? Big data and smart urbanism. GeoJournal 79(1):1–14CrossRefGoogle Scholar
  20. 20.
    Fan W, Bifet A (2013) Mining Big Data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5CrossRefGoogle Scholar
  21. 21.
    Meijer A, Bolívar MPR (2016) Governing the smart city: a review of the literature on smart urban governance. Int Rev Admin Sci 82(2):392–408CrossRefGoogle Scholar
  22. 22.
    Agrawal, D, El Abbadi A, Antony S, Das S (2010) Data management challenges in cloud computing infrastructures. In: International workshop on databases in networked information systems. Springer, Berlin, pp 1–10Google Scholar
  23. 23.
    Azevedo, DNR, de Oliveira JMP (2009) Application of data mining techniques to storage management and online distribution of satellite images.” In Innovative Applications in Data Mining, pp 1–15. Springer, Berlin, 2009Google Scholar
  24. 24.
    Buza K, Nagy G, Nanopoulos A (2014) Storage-optimizing clustering algorithms for high-dimensional tick data. Expert Syst Appl 41(9):4148–4157CrossRefGoogle Scholar
  25. 25.
    Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. ACM SIGOPS Oper Syst Rev 44(2):35–40CrossRefGoogle Scholar
  26. 26.
    The Apache Software Foundation. Apache HBase.
  27. 27.
    Halevi G, Moed H (2012) The evolution of big data as a research and scientific topic: overview of the literature. Res Trends 30:3–6Google Scholar
  28. 28.
    Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  29. 29.
    Cavoukian A, Chibba M, Williamson G, Ferguson A (2015) The importance of ABAC: attribute-based access control to big data: privacy and context. Privacy and Big Data Institute, Ryerson University, Toronto, CanadaGoogle Scholar
  30. 30.
    White T (2009) Hadoop: the definite guide, 1st edn. OReilly Media Inc, SebastopolGoogle Scholar
  31. 31.
    McGilvray D (2008) Executing data quality projects: ten steps to quality data and trusted informationTM. ElsevierGoogle Scholar
  32. 32.
    Keim, DA et al (2006) Challenges in visual data analysis. In: 10th international conference on information visualisation (IV’06). IEEEGoogle Scholar
  33. 33.
    Jaseena KU, David JM (2014) Issues, challenges, and solutions: big data mining. Comput Sci Inf Technol (CS & IT) 131–140Google Scholar
  34. 34.
    Nasser T, Tariq RS (2014) Big data challenges. J Comput Eng Inf Technol 4:3. 9307(2); Nasser T, Tariq RS (2015) Big data challenges. J Comput Eng Inf Technol 4:3. 9307(2)

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Wadi Addawasir, RiyadhSaudi Arabia
  2. 2.Department of of Computer ScienceSPMVVTirupatiIndia

Personalised recommendations