Skip to main content
SpringerLink
Log in
Book cover

Information Technology - New Generations pp 33–42Cite as

Event-Based Anomalies in Big Data

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 558))

Abstract

The data stream generated in a social network or geophysical related or network flow is high speed, continuous, multi-dimensional, and contains massive data. Analytics require the insight behavior of the data stream. The government and business giants want to catch the exceptions to reveal the anomalies and take immediate action. To catch up the exceptions, the analysts need to identify the patterns in a single view of data stream trends, exceptions and catch up anomalies before the system collapses. In this paper, we present a system that detects the variations in the area of interest of data stream. The current research includes the classification of the data stream, detect the event type, commonly used detection methods, and interpret the detected events.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Berwald, J. (2015, August). Deep learning for anomaly detection. PIMS-IMA math modeling in industry XIX.. http://www.mathtube.org/lecture/video/deep-learning-image-anomaly-detection

  2. Szegedy, C., Toshev, A., & Erhan, D. (2013). Deep neural networks for object detection. In Burges, C. J. C., Bottou, L., Ghahramani, Z., & Weinberger, K. Q. (Eds.). NIPS (pp. 2553–2561). Lake Tahoe: Advances in Neural Information Processing 26.

    Google Scholar 

  3. Rettig, L., Khayati, M., Cudre-Mauroux, P., & Piorkowski, M. (2015, November). Online anomaly detection over big data streams. IEEE International Conference on Big Data, pp. 1113–1122.

    Google Scholar 

  4. Hays, M., & Capretz, M. A. (2014). Contextual anomaly detection in big sensor data. Journal of Big Data, 1, 64–71.

    Google Scholar 

  5. Hao, M. C., Dayal, U., & Keim, D. A. (2009). Visual analytics of anomaly detection in large data streams. SPIE 7243, Visualization and Data Analysis 2009, 72430B, 1–10.

    Google Scholar 

  6. Bologa, R., & Florea, A. (2013). Big data and specific analysis methods for insurance fraud detection. Database Systems Journal, 4(4), 30–39.

    Google Scholar 

  7. Parveen, P. (2013, December). Evolving insider threat detection using stream analytics and big data. PhD thesis, University of Texas, Dallas.

    Google Scholar 

  8. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41, 1–72.

    Article  Google Scholar 

  9. Kakavand, M., Mustapha, N., Mustapha, A., & Abdullah, M. (2014). A text mining-based an0maly detection model in network security. Global Journal of Computer Science and Technology, 14, 21–31.

    Google Scholar 

  10. Wang, W., Zhou, X., Zhang, B., & Mu, J. (2013, Febraury). Anamoly detection in big data from UMB radars. Security Communication Networks, 8(14), 2469–2475.

    Article  Google Scholar 

  11. Purdy, S. The science of anomaly detection. White paper. https://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf

  12. Reddy, Y. B. & Hill, D. (2015, November). Document selection using mapreduce. IJSPTM.

    Google Scholar 

  13. Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. Proceedings of the 19th international conference on WWW, 2010, New York, pp. 851–860.

    Google Scholar 

  14. Metzler, D.,Cai, C., & Hovy, E. (2012). Structured event retrieval over microblog archives. HLT-NAACL, 2012, pp. 646–655.

    Google Scholar 

  15. Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: a seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.

    Google Scholar 

  16. Gurarie, E. Behavioral change point analysis in R: The bcpa package. ‘bcpa’ package is available on CRAN, from 2013 available from install in R.

    Google Scholar 

Download references

Acknowledgments

The research work was supported by the AFRL Collaboration Program – Sensors Research, Air Force Contract FA8650-13-C-5800, through subcontract number GRAM 13-S7700-02-C2. The author wishes to express appreciation to Dr. Connie Walton, Director of Sponsored Programs Grambling State University for her continuous support in research

Author information

Authors and Affiliations

  1. Department of Computer Science, Grambling State University, Grambling, LA, 71245, USA

    Yenumula B. Reddy

Authors
  1. Yenumula B. Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yenumula B. Reddy .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Nevada, USA

    Shahram Latifi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Reddy, Y.B. (2018). Event-Based Anomalies in Big Data. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 558. Springer, Cham. https://doi.org/10.1007/978-3-319-54978-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54978-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54977-4

  • Online ISBN: 978-3-319-54978-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation