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Big Data Analytics Platforms for Real-Time Applications in IoT

  • Yogesh Simmhan
  • Srinath Perera
Chapter

Abstract

Big data platforms have predominantly focused on the volume aspects of large-scale data management. The growing pervasiveness of Internet of Things (IoT) applications, along with their associated ability to collect data from physical and virtual sensors continuously, highlights the importance of managing the velocity dimension of big data too. In this chapter, we motivate the analytics requirements of IoT applications using several practical use cases, characterize the trade-offs between processing latency and data volume capacity of contemporary big data platforms, and discuss the critical role that Distributed Stream Processing and Complex Event Processing systems play in addressing the analytics needs of IoT applications.

Keywords

Event Stream Complex Event Processing Stream Processing System Smart Power Grid Ball Possession 
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.

References

  1. 1.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Fut Gen Comput Syst 29(7):1645–1660. ISSN:0167-739X, http://dx.doi.org/10.1016/j.future.2013.01.010
  2. 2.
    Siano P (2014) Demand response and smart grids—A survey. Renew Sustain Energy Rev 30:461–478. ISSN:1364-0321, http://dx.doi.org/10.1016/j.rser.2013.10.022
  3. 3.
    Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32. doi: 10.1109/JIOT.2014.2306328 CrossRefGoogle Scholar
  4. 4.
    Gartner Says 4.9 Billion connected “Things” will be in use in 2015. Press Release. http://www.gartner.com/newsroom/id/2905717. Accessed 11 Nov 2015
  5. 5.
    #IoTH: The internet of things and humans, Tim O’Reilly. O’Reilly Radar. http://radar.oreilly.com/2014/04/ioth-the-internet-of-things-and-humans.html. Accessed 16 April 2014
  6. 6.
    Barnaghi P, Sheth A, Henson C (2013) From Data to Actionable Knowledge: Big Data Challenges in the Web of Things [Guest Editors’ Introduction]. IEEE Intell Syst 28(6):6, 11. doi: 10.1109/MIS.2013.142
  7. 7.
    Lorenz E (1972) Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas? AAASGoogle Scholar
  8. 8.
    Laney D (2001) 3D data management: controlling data volume, velocity and variety. GartnerGoogle Scholar
  9. 9.
    Simmhan Y, Aman S, Kumbhare A, Rongyang L, Stevens S, Qunzhi Z, Prasanna V (2013) Cloud-based software platform for big data analytics in smart grids. Comput Sci Eng 15(4):38,47. doi: 10.1109/MCSE.2013.39
  10. 10.
    El Faouzi N-E, Leung H, Kurian A (2011) Data fusion in intelligent transportation systems: progress and challenges—A survey. Inf Fusion 12(1):4–10. ISSN:1566-2535, http://dx.doi.org/10.1016/j.inffus.2010.06.001
  11. 11.
    Ma J, Zhou X, Li S, Li Z (2011) Connecting agriculture to the internet of things through sensor networks. Internet of Things (iThings/CPSCom). In: International conference on cyber, physical and social computing, pp 184,187, 19–22 Oct 2011. doi: 10.1109/iThings/CPSCom.2011.32
  12. 12.
    Serrano L, González-Flor C, Gorchs G (2010) Assessing vineyard water status using the reflectance based Water Index. Agric Ecosyst Environ 139(4):490–499. ISSN:0167-8809, http://dx.doi.org/10.1016/j.agee.2010.09.007. Accessed 15 Dec 2010
  13. 13.
  14. 14.
    Lewis M (2003) Moneyball: the art of winning an unfair game. W. W. Norton & CompanyGoogle Scholar
  15. 15.
    Adventures in self-surveillance, aka the quantified self, aka extreme Navel-Gazing, Kashmir Hill. Forbes Mag. Accessed 7 April 2011Google Scholar
  16. 16.
    Suresh V, Ezhilchelvan P, Watson P, Pham C, Jackson D, Olivier P (2011) Distributed event processing for activity recognition. In: ACM International conference on Distributed event-based system (DEBS)Google Scholar
  17. 17.
    Rao H, Saxena D, Kumar S, Sagar GV, Amrutur B, Mony P, Thankachan P, Shankar K, Rao S, Rekha Bhat S (2014) Low power remote neonatal temperature monitoring device. In: International conference on biomedical electronics and systems (BIODEVICES), 3–6 March 2014Google Scholar
  18. 18.
    Malone M (2012) Did Wal-Mart love RFID to death? ZDNet. http://www.zdnet.com/article/did-wal-mart-love-rfid-to-death/. Accessed 14 Feb 2012
  19. 19.
    The Nexus of Forces in Action—Use-Case 1: Retail Smart Store, The Open Platform 3.0™ Forum. The Open Group. March 2014Google Scholar
  20. 20.
    Kephart JO, Chess DM (2003) The vision of autonomic computing. IEEE Comput 36(1):41–50. doi: 10.1109/MC.2003.1160055 CrossRefGoogle Scholar
  21. 21.
    Cugola G, Margara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv 44, 3:Article 15Google Scholar
  22. 22.
    Jayasekara S, Kannangara S, Dahanayakage T, Ranawaka I, Perera S, Nanayakkara V (2015) Wihidum: distributed complex event processing. J Parallel Distrib Comput 79–80:42–51. ISSN:0743-7315, http://dx.doi.org/10.1016/j.jpdc.2015.03.002
  23. 23.
    Govindarajan N, Simmhan Y, Jamadagni N, Misra P (2014) Event processing across edge and the cloud for internet of things applications. In: International Conference on Management of Data (COMAD)Google Scholar
  24. 24.
    Wickramaarachchi C, Simmhan Y (2013) Continuous dataflow update strategies for mission-critical applications. In: IEEE International Conference on eScience (eScience)Google Scholar
  25. 25.
    Zhou Q, Simmhan Y, Prasanna VK (2012) Incorporating semantic knowledge into dynamic data processing for smart power grids. In: International semantic web conference (ISWC)Google Scholar
  26. 26.
    Perera S (2013) Solving DEBS 2013 grand challenge with WSO2 CEP/Siddhi. Blog Post. http://srinathsview.blogspot.in/2013/05/solving-debs-2013-grand-challenge-with.html
  27. 27.
    Wu S, Kumar V, Wu KL, Ooi BC (2012) Parallelizing stateful operators in a distributed stream processing system: how, should you and how much? In: ACM International conference on distributed event-based systems (DEBS). http://doi.acm.org/10.1145/2335484.2335515
  28. 28.
    Akidau T et al (2013) MillWheel: fault-tolerant stream processing at internet scale. In: Proceedings of the VLDB endowment, pp 1033–1044Google Scholar
  29. 29.
    Skarlatidis A (2014) Event recognition under uncertainty and incomplete data. Doctoral thesis, University of PiraeusGoogle Scholar
  30. 30.
    Wasserkrug, S et al (2008) Complex event processing over uncertain data. In: International conference on Distributed event-based systems (DEBS). ACMGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computational and Data SciencesIndian Institute of ScienceBangaloreIndia
  2. 2.WSO2ColomboSri Lanka

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