Complex Event Processing in Big Data Systems



Complex event processing is used to solve many problems arising in interdisciplinary areas of computing where data is gathered from different sources and at different intervals. The advent of IoT has necessitated this approach of processing data in real time rather than using a store and compute model. In this chapter, we classify various complex event processing mechanisms and describe complex event models.


Event Processing Query Plan Valid Time Sport Video Complex Event Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abadi DJ, Ahmad Y, Balazinska M, Cetintemel U, Cherniack M, Hwang J-H, Lindner W et al (2005) The design of the borealis stream processing engine. In CIDR 5:277–289Google Scholar
  2. 2.
    Abadi DJ, Carney D, Çetintemel U, Cherniack M, Convey C, Lee S, Stonebraker M, Tatbul N, Zdonik S (2003) Aurora: a new model and architecture for data stream management. VLDB J: Int J Very Large Data Bases 12(2):120–139CrossRefGoogle Scholar
  3. 3.
    Adi A, Etzion O (2004) Amit-the situation manager. VLDB J: Int J Very Large Data Bases 13(2):177–203CrossRefMATHGoogle Scholar
  4. 4.
    Allemang D, Hendler J (2008) Semantic web for the working ontologist. Morgan KaufmanGoogle Scholar
  5. 5.
    Ananthanarayanan R, Basker V, Das S, Gupta A, Jiang H, Qiu T, Reznichenko A, Ryabkov D, Singh M, Venkataraman S (2013) Photon: Fault-tolerant and scalable joining of continuous data streams. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data, pp 577–588. ACMGoogle Scholar
  6. 6.
    Aniello L, Baldoni R, Querzoni L (2013) Adaptive online scheduling in storm. In: Proceedings of the 7th ACM international conference on distributed event-based systems, pp 207–218. ACMGoogle Scholar
  7. 7.
  8. 8.
    Artale A, Franconi E (2009) Foundations of temporal conceptual data models. In: Conceptual modelling: foundations and applications, pp 10–35. Springer, BerlinGoogle Scholar
  9. 9.
    Cetintemel U (2003) The aurora and medusa projects. Data Eng 51(3)Google Scholar
  10. 10.
    Chandrasekaran S, Cooper O, Deshpande A, Franklin MJ, Hellerstein JM, Hong W, Krishnamurthy S, Madden SR, Reiss F, Shah MA (2003) TelegraphCQ: continuous dataflow processing. In: Proceedings of the 2003 ACM SIGMOD international conference on Management of data, pp 668–668. ACMGoogle Scholar
  11. 11.
    Combi C, Degani S, Jensen CS (2008) Capturing temporal constraints in temporal ER models. In: Conceptual Modeling-ER 2008. Springer, Berlin, pp 397–411Google Scholar
  12. 12.
    Cugola Gianpaolo, Margara Alessandro (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv (CSUR) 44(3):15CrossRefGoogle Scholar
  13. 13.
    Demers AJ, Gehrke J, Panda B, Riedewald M, Sharma V, White WM (2007) Cayuga: a general purpose event monitoring system. CIDR 7:412–422Google Scholar
  14. 14.
  15. 15.
    Elmasri R, Navathe SB (2014) Fundamentals of database systems. Pearson,MATHGoogle Scholar
  16. 16.
    Eyers D, Freudenreich T, Margara A, Frischbier S, Pietzuch P, Eugster P (2012) Living in the present: on-the-fly information processing in scalable web architectures. In: Proceedings of the 2nd international workshop on cloud computing platforms, p 6. ACMGoogle Scholar
  17. 17.
    Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L (2012) Sweetening ontologies with DOLCE. In: Knowledge engineering and knowledge management: Ontologies and the semantic Web. Springer, Berlin, pp 166–181Google Scholar
  18. 18.
    Gedik B, Andrade H, Wu KL, Yu PS, Doo M (2008) SPADE: the system S declarative stream processing engine. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, pp 1123–1134Google Scholar
  19. 19.
    Gregersen H (2006) The formal semantics of the timeER model. In: Proceedings of the 3rd Asia-Pacific conference on Conceptual modelling, vol 53. Australian Computer Society, Inc., pp 35–44Google Scholar
  20. 20.
    Gupta A, Jain R (2011) Managing event information: Modeling, retrieval, and applications. Synth Lect Data Manag 3(4):1–141MathSciNetCrossRefGoogle Scholar
  21. 21.
    Gutierrez C, Hurtado C, Vaisman A (2005) Temporal rdf. In: The semantic web: research and applications. Springer, Berlin, pp 93–107Google Scholar
  22. 22.
    Johnson T, Shkapenyuk V, Hadjieleftheriou M (2015) Data stream warehousing in Tidalrace. CIDRGoogle Scholar
  23. 23.
    Lam W, Liu L, Prasad STS, Rajaraman A, Vacheri Z, Doan AH (2012) Muppet: MapReduce-style processing of fast data. Proc VLDB Endowment 5(12):1814–1825CrossRefGoogle Scholar
  24. 24.
    Leibiusky J, Eisbruch G, Simonassi D (2012) Getting started with storm. O’Reilly Media, Inc.Google Scholar
  25. 25.
    Gianmarco De Francisci M, Bifet A (2015) SAMOA: scalable advanced massive online analysis. J Mach Learn Res 16:149–153Google Scholar
  26. 26.
    Motwani R, Widom J, Arasu A, Babcock B, Babu S, Datar M, Manku G, Olston C, Rosenstein J, Varma R Query processing, resource management, and approximation in a data stream management system. CIDRGoogle Scholar
  27. 27.
    Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: 2010 IEEE International conference on data mining workshops (ICDMW), pp 170–177. IEEEGoogle Scholar
  28. 28.
    Papadimitriou S, Sun J, Faloutsos C (2005) Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st international conference on Very large data bases, pp 697–708. VLDB EndowmentGoogle Scholar
  29. 29.
    Perry M, Hakimpour F, Sheth A (2006) Analyzing theme, space, and time: an ontology-based approach. In: Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, pp 147–154. ACMGoogle Scholar
  30. 30.
    Scherp A, Franz T, Saathoff C, Staab S (2015) Accessed 4 Sept 2015
  31. 31.
    Scherp A, Franz T, Saathoff C, Staab S (2009) F–a model of events based on the foundational ontology dolce + DnS ultralight. In: Proceedings of the fifth international conference on Knowledge capture, pp 137–144. ACMGoogle Scholar
  32. 32.
    Sheth A, Perry M (2008) Traveling the semantic web through space, time, and theme. IEEE Internet Comput 12(2):81–86CrossRefGoogle Scholar
  33. 33.
    Sony Corporation, Leading semiconductor wafer surface cleaning technologies that support the next generation of semiconductor devices.
  34. 34.
    Stonebraker M, Çetintemel U, Zdonik S (2005) The 8 requirements of real-time stream processing. ACM SIGMOD Rec 34(4):42–47CrossRefGoogle Scholar
  35. 35.
    Van Hage WR, Malaisé V, Segers R, Hollink L, Schreiber G (2011) Design and use of the Simple Event Model (SEM). Web Semant: Sci, Serv Agents World Wide Web 9(2):128–136CrossRefGoogle Scholar
  36. 36.
    Westermann U, Jain R (2006) E—A generic event model for event-centric multimedia data management in eChronicle applications. In: Proceedings 22nd International conference on data engineering workshops, 2006, pp x106–x106. IEEEGoogle Scholar
  37. 37.
    Westermann U, Jain R (2007) Toward a common event model for multimedia applications. IEEE Multim 1:19–29CrossRefGoogle Scholar
  38. 38.
    Wu E, Diao Y, Rizvi S (2006) High-performance complex event processing over streams. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp 407–418. ACMGoogle Scholar
  39. 39.
    Zaharia M, Das T, Li H, Hunter T, Shenker S, Stoica I (2013) Discretized streams: Fault-tolerant streaming computation at scale. In: Proceedings of the Twenty-Fourth ACM symposium on operating systems principles, pp 423–438. ACMGoogle Scholar
  40. 40.
    Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX Association, pp 2–2Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.PES UniversityBangaloreIndia

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