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Towards a Big Data Analytics Framework for IoT and Smart City Applications

  • Martin StrohbachEmail author
  • Holger Ziekow
  • Vangelis Gazis
  • Navot Akiva
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 4)

Abstract

An increasing amount of valuable data sources, advances in Internet of Things and Big Data technologies as well as the availability of a wide range of machine learning algorithms offers new potential to deliver analytical services to citizens and urban decision makers. However, there is still a gap in combining the current state of the art in an integrated framework that would help reducing development costs and enable new kind of services. In this chapter, we show how such an integrated Big Data analytical framework for Internet of Things and Smart City application could look like. The contributions of this chapter are threefold: (1) we provide an overview of Big Data and Internet of Things technologies including a summary of their relationships, (2) we present a case study in the smart grid domain that illustrates the high-level requirements towards such an analytical Big Data framework, and (3) we present an initial version of such a framework mainly addressing the volume and velocity challenge. The findings presented in this chapter are extended results from the EU funded project BIG and the German funded project PEC.

Keywords

Smart City Hadoop Distribute File System MapReduce Framework Complex Event Processing Batch Layer 
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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Strohbach
    • 1
    Email author
  • Holger Ziekow
    • 1
  • Vangelis Gazis
    • 1
  • Navot Akiva
    • 1
  1. 1.AGT InternationalDarmstadtGermany

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