Activity and Behavior Analytics for Big Data using Parallel and Distributed Hadoop Ecosystem

Authors(2) :-Dr.Kalli Srinivasa Nageswara Prasad, D.Srikar

Time and technology has its own role model with respect to the innovation. Technology and its model view to made things simpler for the end user; where the client need the pattern of the activity related to its domain. Information of extreme size diversity and complexity is everywhere. This disruptive phenomenon is destined to help organizations drive innovation by gaining new and faster insight into their customers. Hence, in this paper we try to put the glimpse of the big data search mechanism in order to use the stochastic automata to see the graph or in other from which may be relevant to the client. In this aspect we have used the parallel computing the logs which already mined and transaction data in various domains in order to give a statistical data to the end user. It can be used in both the way of prevention is better than care in order to make the things smarter and better way. In this paper we have considered both the automata theory to implement the stochastic automata using Hadoop giving raise the concept of efficiency, robustness and accuracy.

Authors and Affiliations

Dr.Kalli Srinivasa Nageswara Prasad
Professor, CSE Department, GVVR Institute of Technology, Bhimavaram, Andhra Pradesh, India
M.Tech., Assistant professor, CSE Department, GVVR Institute of Technology, Bhimavaram, Andhra Pradesh, India

Activity detection, Data Lake, temporal stochastic automata, Hadoop, Distributed computing, Hadoop, Distributed file system

  1. G Palshikar and M. Apte, “Collusion set detection using graph clustering,” Data Knowl. Eng., vol. 16, no. 1, pp. 135–164, 2008.
  2. M Albanese, A. Pugliese, and V. S. Subrahmanian, “Fast activity detection: Indexing for temporal stochastic automaton-based activity models,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 2, pp. 360–373, Feb. 2013.
  3. M Albanese, V. Moscato, A. Picariello, V. S. Subrahmanian, and O. Udrea, “Detecting stochastically scheduled activities in video,” in Proc. IJCAI, M. M. Veloso, Ed. San Francisco, CA, USA, 2007,pp. 1802–1807.
  4. S Lühr, H. H. Bui, S. Venkatesh, and G. A. W. West, “Recognition of human activity through hierarchical stochastic learning,” in Proc. PerCom., Fort Worth, TX, USA, Mar. 2003, pp. 416–422.
  5. T Duong, H. Bui, D. Phung, and S. Venkatesh, “Activity recognition and abnormality detection with the switching hidden semi-Markov model,” in Proc. IEEE CVPR, Washington, DC, USA, 2005.
  6. T V. Duong, D. Q. Phung, H. H. Bui, and S. Venkatesh, “Efficient duration and hierarchical modeling for human activity recognition,” Artif. Intell., vol. 173, no. 7–8, pp. 830–856, May 2009.
  7. R Hamid, Y. Huang, and I. Essa, “ARGMode activity recognition using graphical models,” in Proc. IEEE CVPR, Madison, WI, USA, 2003.
  8. M Albanese, S. Jajodia, A. Pugliese, and V. S. Subrahmanian, “Scalable analysis of attack scenarios,” in Proc. ESORICS, Leuven, Belgium, 2011, pp. 416–433.
  9. M L. Fredman and R. E. Tarjan, “Fibonacci heaps and their uses in improved network optimization algorithms,” in Proc. FOCS, 1984, pp. 338–346.
  10. A. Guttman, “R-trees: A dynamic index structure for spatial searching,” in Proc. SIGMOD Conf., B. Yormark, Ed. New York, NY, USA, 1984, pp. 47–57.
  11. Y. Manolopoulos, A. Nanopoulos, A. N. Papadopoulos, and Y. Theodoridis, “R-trees: Theory and applications,” in Advanced Information and Knowledge Processing. Secaucus, NJ, USA: Springer-Verlag, 2005.
  12. N. Roussopoulos and D. Leifker, “Direct spatial search on pictorial databases using packed R-trees,” in Proc. SIGMOD Conf., S. B. Navathe, Ed., New York, NY, USA, 1985, pp. 17–31.
  13. D. R. Karger and C. Stein, “A new approach to the minimum cut problem,” J. ACM, vol. 43, no. 4, pp. 601–640, 1996.
  14. F. Mörchen, “Unsupervised pattern mining from symbolic temporal data,” SIGKDD Explor. Newslett., vol. 9, no. 1, pp. 41–55, Jun. 2007.
  15. K. Seymore, A. McCallum, and R. Rosenfeld, “Learning hidden Markov model structure for information extraction,” in Proc. AAAI Workshop Machine Learning for Information Extraction, 1999.
  16. M. Albanese et al., “A constrained probabilistic petri net framework for human activity detection in video,” IEEE Trans. Multimedia, vol. 10, no. 8, pp. 1429–1443, Dec. 2008.
  17. V. Vu, F. Brémond, and M. Thonnat, “Automatic video interpretation: A novel algorithm for temporal scenario recognition,” in Proc. IJCAI, San Francisco, CA, USA, Aug. 2003, pp. 1295–1302.
  18. L. Golab and M. T. Özsu, “Issues in data stream management,” SIGMOD Rec., vol. 32, pp. 5–14, Jun. 2003 [Online]. Available:

Publication Details

Published in : Volume 4 | Issue 6 | January-February 2018
Date of Publication : 2018-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 179-182
Manuscript Number : IJSRSET184830
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Dr.Kalli Srinivasa Nageswara Prasad, D.Srikar , " Activity and Behavior Analytics for Big Data using Parallel and Distributed Hadoop Ecosystem, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 6, pp.179-182, January-February-2018.
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