Time Series Distributed Analysis in IoT with ETL and Data Mining Technologies

The paper describes an approach to performing a distributed analysis on time series. The approach suggests to integrate Data Mining and ETL technologies and to perform primary analysis of time series based on a subset of data sources (primary data sources). Other data sources are only used if it is necessary to obtain additional information. This allows to reduce the number of requests to data sources and network traffic. In the result it makes it possible to use communication channels with low bandwidth (including wireless networks) for data collection.

[1]  Greg Byrd,et al.  The Internet of Everything , 2017, Computer.

[2]  Ralph Kimball,et al.  The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data , 2004 .

[3]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[4]  Margot Brereton,et al.  An internet of social things , 2014, OZCHI.

[5]  I. I. Kholod,et al.  Smart collection of data for financial instruments , 2017, 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM).

[6]  Antonio Iera,et al.  The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization , 2012, Comput. Networks.

[7]  Ken Barker,et al.  Distributed data warehouse architecture and design , 2000 .

[8]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[9]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[10]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[11]  Nathan Marz,et al.  Big Data: Principles and best practices of scalable realtime data systems , 2015 .

[12]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[13]  Karolj Skala,et al.  Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing , 2015, Open J. Cloud Comput..

[14]  Stefan Müller,et al.  Pentaho Data Integration , 2014 .

[15]  Luis M. Candanedo,et al.  Data driven prediction models of energy use of appliances in a low-energy house , 2017 .

[16]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[17]  Athanasios V. Vasilakos,et al.  Future Internet of Things: open issues and challenges , 2014, Wireless Networks.

[18]  W. H. Inmon,et al.  Building the data warehouse (2nd ed.) , 1996 .