Big Data Layers and Analytics: A Survey

  • G. ManikandanEmail author
  • S. Abirami
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


With the advancement in the Internet technologies, the amount of data in the world has been increased dramatically. Due to this large volume of data, the traditional data analysis method fails to handle and also it is difficult to store, capture, curation, search, analyze and visualization of data. To deal with this issue we adequate tools, techniques, algorithms, architecture and the design principles. Extraction of the key information from the large data is the major issue in the big data analytical community. To discuss this issue in detail, this paper begins with the brief introduction about the big data, characteristics and structure of the big data, followed by addressing the various layers big data value chain in detail. And also this paper provides the comprehensive survey about the types and sub-types of the analytical methods of big data.


Big data analytics Data storage Analytical methods Data layers Data acquisition Social media analytics Audio/Video analytics 


  1. 1.
    Han Hu, Yong Gang Wentat-Seng Chua, Xuelong Li, “Toward Scalable Systems for Big Data Analytics: A Technology Tutorial”, IEEE access practical innovation and smart solutions, Volume 2, pp. 652–687 (2014).Google Scholar
  2. 2.
    Manikandan G, Abirami S,2016, “A Survey: 12 V’s, Challenges and Issues in Big Data” International Conference on Distributed Intelligent Computing, pp. 887–897, (2016).Google Scholar
  3. 3.
    Min Chen, Shiwen Mao and Yunhao Liu, Big Data: A Survey, Mobile Netw Appl, Springer Science + Business Media, vol. 19, pp. 171–209 (2014).Google Scholar
  4. 4.
    Kumar Ravi, Vadlamani Ravi, A survey on opinion mining and sentiment analysis: Tasks, approaches and applications, Knowledge-Based Systems vol. 89, pp. 14–46, (2015).Google Scholar
  5. 5.
    Amy Bruckman and J. Weiss, Analysis of Log File Data to Understand Behavior and Learning in an Online Community The International Handbook of Virtual Learning Environments, Springer, pp. 1449–1465 (2006).Google Scholar
  6. 6.
    Castillo. Effective web crawling, ACM SIGIR Forum, vol. 39, pp. 55–56, (2005).Google Scholar
  7. 7.
    Amir Gandomi, Murtaza Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management vol. 35, pp. 137–144, (2015).Google Scholar
  8. 8.
    Lam, C.F, Fiber optic communication technologies: what’s needed for datacenter network operations. IEEE Commun. Mag. Vol. 48, pp. 32–39 (2012).Google Scholar
  9. 9.
    Fatemeh Ahmadi-Abkenari, Ali Selamat, An architecture for a focused trend parallel Web crawler with the application of click stream analysis, Information Sciences vol. 184, pp. 266–281, (2012).Google Scholar
  10. 10.
    Aisha Siddiqa, Ibrahim Abaker, Targio Hashem, Ibrar Yaqoob, Mohsen Marjani, Shahabuddin Shamshirband, Abdullah Gani, Fariza Nasaruddin, A survey of big data management: Taxonomy and state-of-the-art. Journal of Network and Computer Applications, doi: 10.1016/j.jnca.2016.04.008, (2016).
  11. 11.
    Sapna Dev, Dr. Arvind Kalia, Study of Data Cleaning & Comparison of Data Cleaning Tools, International Journal of Computer Science and Mobile Computing, Vol. 4, Issue. 3, pp. 360–370, (2015).Google Scholar
  12. 12.
    Winda Astriani, Rina Trisminingsih, Extraction, Transformation, and Loading (ETL) module for hotspot spatial data warehouse using geokettle, Procedia Environmental Sciences vol. 33, pp. 626–634, (2016).Google Scholar
  13. 13.
    Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat, Waleed Kamaleldin Mahmoud Ali, Muhammad Alam, Muhammad Shiraz, and Abdullah Gani, 2014, Big Data: Survey, Technologies, Opportunities, and Challenges, pp 1–18, (2014) Article ID 712826.
  14. 14.
    Jiang J, Information extraction from text. In Mining text data United States: Springer, pp. 11–41, (2012).Google Scholar
  15. 15.
    Muhammad Habib ur Rehmana, Victor Chang, Aisha Batool, Teh Ying Waha, Big data reduction framework for value creation in sustainable enterprises, International Journal of Information Management vol. 36, pp. 917–928, (2016).Google Scholar
  16. 16.
    C. Bosco, V. Patti, A. Bolioli, Developing corpora for sentiment analysis: The case of irony and senti-tut, IEEE Intell. Syst. Vol. 2, pp. 55–63. (2013).Google Scholar
  17. 17.
    Y. Dang, Y. Zhang, H. Chen, A lexicon-enhanced method for sentiment classification: an experiment on online product reviews, IEEE Intell. Syst. Vol. 25. pp. 46–53, (2010).Google Scholar
  18. 18.
    S.K. Li, Z. Guan, L.Y. Tang, Exploiting consumer reviews for product feature ranking, J. Comput. Sci. Technol. Vol. 27, pp. 635–649, (2012).Google Scholar
  19. 19.
    A.C.-R. Tsai, C.-E. Wu, R.T.-H. Tsai, J.Y.-J. Hsu, Building a concept-level sentiment dictionary based on commonsense knowledge, IEEE Intelligent System, Vol. 2, pp. 22–30 (2013).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Information Science and TechnologyAnna UniversityChennaiIndia

Personalised recommendations