Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Real Time Monitoring, Alerting and Anomaly Detection Analytics Platform Using Reactive Programming

Author : Jagreet Kaur 1 Dr. Kulwinder Singh Mann 2

Date of Publication :29th November 2017

Abstract: The IT world is moving towards innovation and technology. The growth of data is exponentially increasing in various domains like healthcare, Iot, Biometrics and many more. With periodic batch processing data, it is not possible to provide the required information to take instant decisions. Streaming data analytics is the bloodstream of modern applications. The traditional cloud-based storage model is giving way to do in-memory analytics processing of big data streams. There are many domains where real-time processing of data is used for taking timely decisions that can minimize the risks of human lives and resources, enhance the quality of human lives, increase efficiency of resources management and proficiency, etc. Therefore, Real time Data is required in every field. This brings the requirement of Real time Analytics Platform . Adaptation of Data First Approach is needed for DataDriven applications to address the many issues like removal of Data Silos to create Single Integrated Platform, Complex Data Governance, analytics too time consuming and expensive, High cost on current systems and enabling real time analytics. In this Paper, we will discuss Real Time analytics platform based on Reactive Machine learning ,Functional Programming and Kappa Architecture for Monitoring, Alerting, IoT Based applications or event Driven applications.

Reference :

    1. 1. LI Zhao, ZHANG Chuang, CHEN Meng-meng, XU Ke-fu.,“SpeedStream: a Real-time Stream Processing Platform in the Cloud” International Conference on Computer Engineering, November. 2014.
    2. 2. hao δ, Chuang Z, Ke-Fu X, et al. “A Computing model for Real-Time Stream Processing, Cloud Computing and Big Data”, International Conference on. IEEE, 134-137, 2014.
    3. 3. Zhengping Qian, Yong He, Chunzhi Su, et al. TimeStream: Reliable Stream Computation in the Cloud. EuroSys, 2013:1-14.
    4. 4. Schmidt S., δegler T., Schaller D., et al. Realtime Scheduling for Data Stream management Systems. Real-Time Systems, 2005.
    5. 5. Mishne, G., Dalton, J., Li, Z., Sharma, A., & Lin, J.,. Fast data in the era of big data: Twitter's real-time related query suggestion architecture. In Proceedings of he ACM SIGMOD International Conference on Management of Data (pp. 1147-1158), 2013.
    6. 6. Abraham, B. and Chuang, A., Outlier detection and time series modeling. Technometrics, pp-241–248, 1989.
    7. 7. Abe N., Zadrozny B., A Langford, “Outlier detection by active learning” In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 504–5, 2006.
    8. 8. E. Czaplicki and S. Chong. “Asynchronous functional reactive programming for GUIs. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’13, pages 411–422, New York, NY, USA, 2013
    9. 9. C. Elliott and P. Hudak. “Functional reactive animation” In Proceedings of the Second ACM SIGPLAN International Conference on Functional Programming, ICFP ’97, pages 263–273, New York, NY, USA, 1997.
    10. 10. T. Salmon, D. Bhamare, A. Erbad, R. Jain, M. Samaka, "Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments," The 4th IEEE International Conference on Cyber Security and Cloud Computing (IEEE Cloud 2017), New York, June 26-28, 2017.
    11. 11. Li, H., Achim, A., Bull, D.: “Unsupervised video anomaly detection using feature clustering”, IET Signal Proc. 6, 521–533, 2012.
    12. 12. Fangjin Yang, Eric Tschetter, “Druid: A Realtime Analytical Data Store”, SIGMOD '14 Proceedings of the ACM SIGMOD international conference on Management of data, June 2014
    13. 13. Samy Chambi, Daniel Lemire, Robert Godin, Kamel Boukhalfa, Charles R. Allen, Fangjin Yang, “Optimizing Druid with Roaring bitmaps”, IDEAS '16: Proceedings of the 20th International Database Engineering & Applications Symposium, July 2016.
    14. 14. Apache Kafka. http://kafka.apache.org/
    15. 15. Fangjin Yang, Eric Tschetter, Xavier Léauté, Nelson Ray, Gian Merlino, Deep Ganguli, “Druid: a realtime analytical data store”, SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, June 2014.
    16. 16. E. Bainomugisha, A. L. Carreton, T. v. Cutsem, S. Mostinckx, and W. d. Meuter. A survey on reactive programming. ACM Comput. Surv., 45(4):52:1–52:34, Aug. 2013. ISSN 0360-0300. doi: 10.1145/2501654.2501666
    17. 17. Laura Rettig, Mourad Khayati,”Online Anomaly Detection over Big Data Streams”, ´IEEE International Conference on Big Data, Switzerland, 2015.

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