Intelligent Systems

  • C.S.R. PrabhuEmail author
  • Aneesh Sreevallabh Chivukula
  • Aditya Mogadala
  • Rohit Ghosh
  • L.M. Jenila Livingston


In Chap. 1, we presented a total overview of Big Data Analytics. In this chapter, we delve deeper into Machine Learning and Intelligent Systems. By definition, an algorithm is a sequence of steps in a computer program that transforms given input into desired output. Machine learning is the study of artificially intelligent algorithms that improve their performance at some task with experience. With the availability of big data, machine learning is becoming an integral part of various computer systems. In such systems, the data analyst has access to sample data and would like to construct a hypothesis on the data. Typically, a hypothesis is chosen from a set of candidate patterns assumed in the data. A pattern is taken to be the algorithmic output obtained from transforming the raw input. Thus, machine learning paradigms try to build general patterns from known data to make predictions on unknown data.


  1. 1.
    U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From Data Mining to Knowledge Discovery: An Overview (AAAI, 1996)Google Scholar
  2. 2.
    K.P. Bennett, E. Parrado-Hernández, The interplay of optimization and machine learning research. J. Mach. Learn. Res. (2006)Google Scholar
  3. 3.
    K. Deb, Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design (1999)Google Scholar
  4. 4.
    P.G.K. Reiser, Computational models of evolutionary learning, in Apprentissage: des principes naturels aux methodes artificielles (1998)Google Scholar
  5. 5.
    J. Zhang, Z.-H. Zhan, Y. Lin, N. Chen, Y.-J. Gong, J.-h. Zhong, H.S.H. Chung, Y. Li, Y.-h. Shi, Evolutionary computation meets machine learning: a survey. Computational Intelligence Magazine (IEEE, 2011)Google Scholar
  6. 6.
    C. Blum, A. Roli, Meta-heuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (2003)Google Scholar
  7. 7.
    U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, The KDD process for extracting useful knowledge from volumes of data. Commun. ACM (1996)Google Scholar
  8. 8.
    P. Gonzlez-Aranda, E. Menasalvas, S. Milln, C. Ruiz, J. Segovia, Towards a methodology for data mining project development: the importance of abstraction, in Data Mining: Foundations and Practice, Studies in Computational Intelligence (2008)Google Scholar
  9. 9.
    J. Lin, C. Dyer, Data-Intensive Text Processing with MapReduce (Morgan and Claypool Publishers, 2010)Google Scholar
  10. 10.
    V. Agneeswaran, Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives (Pearson FT Press, 2014)Google Scholar
  11. 11.
    B. Babcock, S. Babu, M. Datar, R. Motwani, J. Widom, Models and issues in data stream systems, in PODS ’02 (2002)Google Scholar
  12. 12.
    M.M. Gaber, A. Zaslavsky, S. Krishnaswamy, Mining data streams: a review. SIGMOD Rec. (2005)Google Scholar
  13. 13.
    L. Golab, M.T. Özsu, Issues in data stream management. SIGMOD Rec. (2003)Google Scholar
  14. 14.
    P. Misra, Y. Simmhan, J. Warrior, Towards a practical architecture for India centric internet of things. CoRR (2014)Google Scholar
  15. 15.
    N. Kaka, A. Madgavkar, J. Manyika, J. Bughin, P. Parameswaran, India’s Tech opportunity: transforming work, empowering people. McKinsey Global Institute Report (2014)Google Scholar
  16. 16.
    H. Zhuge, The knowledge grid and its methodology, in First International Conference on Semantics, Knowledge and Grid (2005)Google Scholar
  17. 17.
    Euzenat, J., Research challenges and perspectives of the Semantic Web. Intelligent Systems (IEEE, 2002)Google Scholar
  18. 18.
    S.C. Chan, K.M. Tsui, H.C. Wu, Y. Hou, Y.-C. Wu, F.F. Wu, Load/price forecasting and managing demand response for smart grids: methodologies and challenges. Signal Processing Magazine (IEEE, 2012)Google Scholar
  19. 19.
    H. Farhangi, The path of the smart grid. Power and Energy Magazine (IEEE, 2010)Google Scholar
  20. 20.
    S. Ramchurn, D. Sarvapali, P. Vytelingum, A. Rogers, N.R. Jennings, Putting the ‘smarts’ into the smart grid a grand challenge for artificial intelligence. Commun. ACM (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • C.S.R. Prabhu
    • 1
    Email author
  • Aneesh Sreevallabh Chivukula
    • 2
  • Aditya Mogadala
    • 3
  • Rohit Ghosh
    • 4
  • L.M. Jenila Livingston
    • 5
  1. 1.National Informatics CentreNew DelhiIndia
  2. 2.Advanced Analytics InstituteUniversity of Technology, SydneyUltimoAustralia
  3. 3.Saarland UniversitySaarbrückenGermany
  4. 4.Qure.aiGoregaon East, MumbaiIndia
  5. 5.School of Computing Science and EngineeringVellore Institute of TechnologyChennaiIndia

Personalised recommendations