Data analysis as a web service: A case study using IoT sensor data

The advent of Internet of Things, has resulted in the development of infrastructure for capturing and storing data from domains ranging from smart devices (e.g. smartphones) to smart cities. This data is often available publicly and has enabled a wider range of data consumers to utilise such data sets for applications ranging from scientific experimentation to enhancing commercial activity for businesses. Accordingly this has resulted in the need for the development data analysis tools that are both simple to use and provide the most effective tools for a given data set. To this end, we introduce data analysis tools as web service, that enables the data consumer to make a simple HTTP request for processing data over the internet. By providing such tools as a web service, we demonstrate the potential of such a system to aid both the advanced and novice data consumer. Furthermore, this work provides an use case example of the proposed tool on publicly available data extracted from the smart city CityPulse IoT project.

[1]  María Bermúdez-Edo,et al.  On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data , 2016, IEEE Internet of Things Journal.

[2]  Krishnaprasad Thirunarayan,et al.  Extracting City Traffic Events from Social Streams , 2015, ACM Trans. Intell. Syst. Technol..

[3]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[4]  Francisco Herrera,et al.  Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.

[5]  Bin Cheng,et al.  Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander , 2015, 2015 IEEE International Congress on Big Data.

[6]  Ralf Tönjes,et al.  CityPulse: Large Scale Data Analytics Framework for Smart Cities , 2016, IEEE Access.

[7]  François Carrez,et al.  A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things , 2015, IEEE Internet of Things Journal.

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Richard P. Martin,et al.  Poster: Smart buildings, sensor networks, and the Internet of Things , 2011, SenSys.

[10]  Feng Gao,et al.  Complex event service provision and composition based on event pattern matchmaking , 2014, DEBS '14.

[11]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[12]  Payam M. Barnaghi,et al.  An Internet of Things Platform for Real-World and Digital Objects , 2012, Scalable Comput. Pract. Exp..