Fault Tolerance Aware Low Latency Event Detection for Complex Event Processing System

Authors

  • A. Gokila  Department of Computer Science and Engineering, Park College of Engineering and Technology, Kaniyur, Tamilnadu, India
  • R. Janani  Department of Computer Science and Engineering, Park College of Engineering and Technology, Kaniyur, Tamilnadu, India
  • G. T. Kalaiarasi  Department of Computer Science and Engineering, Park College of Engineering and Technology, Kaniyur, Tamilnadu, India

Keywords:

Latency Event Detection, Hadoop, Big Data, HDFS, RFID, GPS

Abstract

Data gathering in the real world environment becomes the most complex process where the multiple sensors and devices are exists. This enormous number of sensors and devices gathers the large number of data’s parallely which leads to complex event processing system. Latency is the biggest problem in the handling of complex event processing system which is resolved in the existing work by introducing the pattern sensitive partitioning model in which latency of the complex event processing system can be reduced considerably with the concern of low parallelization degree. Existing work do not concentrate on handling of fault tolerance where the failure is most concerned issue in the parallel event processing system. Event failed then it is required to process it from the start which would increase the latency more. Problem is overcome in the proposed methodology by introducing the novel approach called the failure aware latency reduced complex event processing system. The experimental tests conducted were proves that the proposed methodology of this work provides better result than the existing work in terms of improved system performance.

References

  1. Buchmann. A and Koldehofe. B, Eds.( 2009), IT-Information Technology. Munich, Germany: Oldenbourg Verlag , vol. 51.
  2. Chlamtac. I, Miorandi. D, Pellegrini. F. D and, Sicari. S (2012), ‘Internet of Things: Vision, applications and research challenges’, Ad Hoc Netw.,vol. 10,  no. 7, pp. 1497–1516.
  3. Cao. B, Giakkoupis. M, Prasanna. V. K,  and Simmhan. Y (2011), ’Adaptive rate Stream processing for smart grid applications on clouds’, in Proc. 2nd Int.Workshop Sci. Cloud Comput. (ScienceCloud’11), pp. 33–38.
  4. Cugola. G and Margara. A (2010), ‘Tesla: A formally defined event specification language’, in Proc. 4th ACM Int. Conf. Distrib. Event-Based Syst.(DEBS’10), pp. 50–61.
  5. Cugola. G and Margara. A (2012), ‘Processing flows of information: From data Stream to complex event processing’, ACM Comput. Surv. vol. 44, no. 3, pp. 15:1–15:62.
  6. Chakravarthy. S and Mishra. D(1994), ‘Snoop: An expressive event specification language for active databases,’ Data Knowl. Eng., vol. 14, no. 1, pp. 1–26.
  7. Charalambous. T, Fiscato. M, Kalyvianaki. E, and Pietzuch. P. (2012),’Overload management in data stream processing systems with latency guarantees’, in Proc. 7th IEEE Int. Workshop Feedback Comput.,.
  8. Fernandez. R. C., Gal. A, Pietzuch. P and Weidlich. M (2014), ‘Scalable stateful Stream processing for smart grids’,  in Proc. 8th ACM Int. Conf. Distrib. Event-Based Syst. (DEBS’14), pp. 276–281.
  9. Jerzak. Z and Ziekow. H (2014),’The debs 2014 grand challenge’, in Proc. 8th ACM Int. Conf. Distrib. Event-Based Syst. (DEBS’14), pp. 266–269.
  10. Koch G. G, Koldehofe. B, and Rothermel. K (2010), ‘Cordies: Expressive event Correlation in distributed systems’ , in Proc. 4th ACM Int. Conf. Distrib. Event-Based Syst. (DEBS’10),  pp. 26–37.

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Published

2016-02-29

Issue

Section

Research Articles

How to Cite

[1]
A. Gokila, R. Janani, G. T. Kalaiarasi, " Fault Tolerance Aware Low Latency Event Detection for Complex Event Processing System, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.506-509, January-February-2016.