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Strategies and Challenges in Big Data: A Short Review

  • D. K. Santhosh KumarEmail author
  • Demian Antony D‘Mello
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

The Big Data is the new trending technology in the field of research in recent years and is not only big in size, but also generated at brisk rate and variety, which endeavors the research upsurge in multidisciplinary fields like Government, Healthcare and business performance applications. Due to the key features (Volume, Velocity, and Variety) of Big Data it’s difficult to store and analyse with conventional tools and techniques. It acquaints unique challenges in scalability, storage, computational complexity, analytical, statistical correlation and security issues. Hence we describe the salient features of big data and how these affects the storage technologies and analytical techniques. We then present the taxonomy of Big Data sub-domains and discuss the different datasets based on data characteristics, privacy concern, and domain and application knowledge. Furthermore, we also explore research issues and challenges in big data storage technologies, privacy of data and data analytics.

Keywords

Big Data Analytics Data science Big Data domains Data security Machine learning Hadoop 

References

  1. 1.
    Jin, X., Wah, B.W., Cheng, X., Wang, Y.: Significance and challenges of big data research. Big Data Res. 2(2), 59–64 (2015)CrossRefGoogle Scholar
  2. 2.
    Mining, D.: Big-data analytics : a critical review and some future directions. Uroš Jovanovi č, Aleš Štimec Daniel Vladuši č Gregor Papa * Jurij Šilc. 10, 337–355 (2015)Google Scholar
  3. 3.
    Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2017)CrossRefGoogle Scholar
  4. 4.
    Du, D., Li, A., Zhang, L.: Survey on the applications of big data in Chinese real estate enterprise. Procedia Comput. Sci. 30, 24–33 (2014)CrossRefGoogle Scholar
  5. 5.
    De Mauroandrea, A., Greco, M., Grimaldim, M., Table, V.: What is big data? A consensual definition and a review of key research topics, p. 97 (2015)Google Scholar
  6. 6.
    Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manag. 35, 137–144 (2015)CrossRefGoogle Scholar
  7. 7.
    Özköse, H., Uõ, P.L.Q., Gencer, C.: Yesterday, today and tomorrow of big data. Procedia-Soc. Behav. Sci. 195, 1042–1050 (2015)CrossRefGoogle Scholar
  8. 8.
    Abaker, I., Hashem, T., Yaqoob, I., Badrul, N., Mokhtar, S., Gani, A., Ullah, S.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Cao, L.: Data science: a comprehensive overview. ACM Comput. Surv. (CSUR) 50(3), 43 (2017)CrossRefGoogle Scholar
  11. 11.
    Tan, W., Blake, M.B., Saleh, I., Dustdar, S.: Social-network-sourced big data analytics. IEEE Internet Comput. 17, 62–69 (2013)CrossRefGoogle Scholar
  12. 12.
    Williams, G.J., Office, A.T.: Big data opportunities and challenges: discussions from data analytics persoectives. Comput. Intell. Mag. IEEE. 9, 62–74 (2014)CrossRefGoogle Scholar
  13. 13.
    Hu, H., Wen, Y., Chua, T.-S., Li, X.: Toward scalable systems for big data analytics: a technology tutorial. IEEE Access. 2, 652–687 (2014)CrossRefGoogle Scholar
  14. 14.
    Kaliyar, R.: Graph databases: a survey, pp. 785–790 (2015)Google Scholar
  15. 15.
    Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R.: Big data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79–80, 3–15 (2015)CrossRefGoogle Scholar
  16. 16.
    Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)CrossRefGoogle Scholar
  17. 17.
    Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. J. Big Data 2, 8 (2014)CrossRefGoogle Scholar
  18. 18.
    Khalifa, S., Elshater, Y., Sundaravarathan, K., Bhat, A.: The six pillars for building big data analytics ecosystems. ACM Comput. Surv. 49, 1–36 (2016)CrossRefGoogle Scholar
  19. 19.
    Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26, 97–107 (2014)CrossRefGoogle Scholar
  20. 20.
    Colombo, P., Ferrari, E.: Privacy aware access control for big data: a research roadmap. Big Data Res. 2, 145–154 (2015)CrossRefGoogle Scholar
  21. 21.
    Rumbold, J.M.M., Pierscionek, B.K.: What are data? A categorization of the data sensitivity spectrum. Big Data Res. 12, 49–59 (2017)CrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Ren, J., Liu, J., Xu, C., Guo, H., Liu, Y.: A survey on emerging computing paradigms for big data. Chin. J. Electron. 26(1), 1–12 (2017)CrossRefGoogle Scholar
  23. 23.
    Khan, N., Yaqoob, I., Abaker, I., Hashem, T., Inayat, Z., Kamaleldin, W., Ali, M., Alam, M., Shiraz, M., Gani, A.: Big Data: Survey, Technologies, Opportunities, and Challenges (2014)Google Scholar
  24. 24.
    Samuel, S.J., Rvp, K., Sashidhar, K., Bharathi, C.R.: A survey on big data and its research challenges. ARPN J. Eng. Appl. Sci. 10, 3343–3347 (2015)Google Scholar
  25. 25.
    Tsai, C.W., Lai, C.F., Chao, H.C., Vasilakos, A.V.: Big data analytics: a survey. J. Big Data 2, 1–32 (2015)CrossRefGoogle Scholar
  26. 26.
    L’Heureux, A., Grolinger, K., Elyamany, H.F., Capretz, M.A.M.: Machine learning with big data: challenges and approaches. IEEE Access. 5, 7776–7797 (2017)CrossRefGoogle Scholar
  27. 27.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: a new data clustering algorithm and its applications. Data Mining Knowl. Discov. 1(2), 141–182 (1997)CrossRefGoogle Scholar
  28. 28.
    Kisilevich, S., Mansmann, F., Keim, D.: P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos, pp. 1–4 (2010)Google Scholar
  29. 29.
    Ordonez, C., Omiecinski, E.: Efficient disk-based k-means clustering for relational databases. IEEE Trans. Knowl. Data Eng. 16, 909–921 (2004)CrossRefGoogle Scholar
  30. 30.
    Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: A Fast Scalable Classifier for Data Mining (1996)Google Scholar
  31. 31.
    Mico, L., Oncina, J., Mic, L., Oncina, J.: Dynamic Insertions in TLAESA fast NN search algorithm (2016)Google Scholar
  32. 32.
    Djouadi, A., Bouktache, E.: A fast algorithm for the nearest-neighbor classifier. IEEE Trans. Pattern Anal. Mach. Intell. 19, 277–281 (1997)CrossRefGoogle Scholar
  33. 33.
    Han, J., Pei, J., Yin, Y.: Frequent Pattern Tree: Design and Construction, pp. 1–12 (2000)Google Scholar
  34. 34.
    Chen, B., Way, H., Francisco, S.S., Haas, P., Jose, S., Scheuermann, P.: A New Two-Phase Sampling Based Algorithm for Discovering Association Rules (2002)Google Scholar
  35. 35.
    Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)CrossRefGoogle Scholar
  36. 36.
    Zaki, M.J., Hsiao, C.: Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans. Knowl. Data Eng. 17, 462–478 (2005)CrossRefGoogle Scholar
  37. 37.
    Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (2001)Google Scholar
  38. 38.
    Masseglia, F., Poncelet, P., Teisseire, M.: Incremental mining of sequential patterns in large databases. Data Knowl. Eng. 46(1), 97–121 (2003)CrossRefGoogle Scholar
  39. 39.
    Huang, J., Lin, S., Chen, M.: DPSP: Distributed Progressive Sequential Pattern Mining on the Cloud, pp. 27–34 (2010)Google Scholar
  40. 40.
    Acharjya, D.P.: A survey on big data analytics: challenges, open research issues and tools. Int. J. Adv. Comput. Sci. Appl. 7, 511–518 (2016)Google Scholar
  41. 41.
    Izakian, H., Abraham, A., Snášel, V.: Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 1690–1694. IEEE Press (2009). ISBN 978-1-4244-5612-3Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • D. K. Santhosh Kumar
    • 1
    Email author
  • Demian Antony D‘Mello
    • 1
  1. 1.Department of Computer Science and Engineering, Canara Engineering College MangaloreVTUBelagaviIndia

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