adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011 Towards a Big Data Analytics Framework for IoT and Smart City Applications Martin Strohbach, Holger Ziekow, Vangelis Gazis, Navot Akiva AGT International Hilpertstrasse 35, 64295 Darmstadt, Germany e-mail: {mstrohbach, hziekow, vgazis, nakiva}@agtinternational.com 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 ser- vices 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 chap- ter 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 chal- lenge. The findings presented in this chapter are extended results from the EU funded project BIG and the German funded project PEC. 1 Introduction In times of increasing urbanization, local decision makers must be prepared to main- tain and increase the quality of life of a growing urban population. For instance, there are major challenges related to minimizing pollution, managing traffic as well as mak- ing efficient use of scarce energy resources. For instance, in regard to congested traf- fic conditions, the Confederation of British Industries estimates that the cost of road congestion in the UK is GBP 20 billion (i.e., USD 38 billion) annually. In addition to challenges related to the efficient use of natural and manmade resources ensuring the health and safety of urban citizens, e.g. in the context of large events or supporting law enforcement are key concerns of a modern smart city. In order to address these challenges urban decision makers as well as citizens will need the capacity to make the right assessment of urban situations based on correct data, and, more importantly, they will need the key information contained in the data to assist them in their decision processes.