| Title |
Towards a Big Data Analytics Framework for IoT and Smart City Applications |
| ID_Doc |
38808 |
| Authors |
Strohbach, M; Ziekow, H; Gazis, V; Akiva, N |
| Title |
Towards a Big Data Analytics Framework for IoT and Smart City Applications |
| Year |
2015 |
| Published |
|
| DOI |
10.1007/978-3-319-09177-8_11 |
| 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. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Book Citation Index – Science (BKCI-S) |
| EID |
WOS:000361961200012 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Operations Research & Management Science |
| Research Area |
Computer Science; Operations Research & Management Science |
| PDF |
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