Abstract
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.
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References
Skala, K., Davidović, D., Afgan, E., Sović, I., Šojat, Z.: Scalable distributed computing hierarchy: cloud, fog and dew computing. Open J. Cloud Comput. (RobPub) 2(1), 16–24 (2015). ISSN 2199-1987
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog Computing and its role in the internet of things. In Processing of MCC (2012), 17 August 2012, Helsinki, Finland, pp. 13–16 (2012)
Ahmed, A., Ahmed, E.: A survey on mobile edge computing. In: 10th IEEE International Conference on Intelligent Systems and Control (ISCO 2016) (2016). doi:10.13140/RG.2.1.3254.7925
Noaman, A.Y.: Distributed data warehouse architecture and design. Ph.D. thesis, University of Manitoba (2000)
Inmon, W.H.: Building the Data Warehouse, 2nd edn. Wiley, New York (1996)
Devlin, B.: Data Warehouses from Architecture to Implementation. Addison–Wesley, Boston (1997)
Kimball, R., Caserta, J.: The Data Warehouse ETL Toolkit. Wiley, New York (2004)
Pentaho Data Integration. http://www.pentaho.com/product/data-integration
CloverETL. http://www.cloveretl.com/products
Talend Open Studio. http://www.talend.com/products/talend-open-studio
Aggrawal, C.C.: Data Streams: Models and Algorithms. Springer, New York (2007)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Manning Publications, Greenwich (2010)
Tsai, C.-W., Lai, C.-F., Vasilakos, A.V.: Future internet of things: open issues and challenges. Wirel. Netw. 20(8), 2201–2217 (2014)
Atzori, L., Iera, A., Morabito, G., Nitti, M.: The social internet of things (SIoT)—when social networks meet the internet of things: concept, architecture and network characterization. Comput. Netw. 56(16), 3594–3608 (2012)
Nansen, B., van Ryn, L., Vetere, F., Robertson, T., Brereton, M., Douish, P.: An internet of social things. In: OzCHI 2014, 02–05 December 2014, Sydney, NSW, Australia (2014). doi:10.1145/2686612.2686624
Evans, D.: The Internet of Everything. Cisco IBSG (2012). http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE.pdf
Gubbi, J., Buyya, R., Marusic, S., Palaniswamia, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Kholod, I.I., Efimova, M.S.: Smart collection of data for financial instruments. In: 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). pp. 705–708. IEEE Conference Publications (2017). doi:10.1109/SCM.2017.7970697
Candanedo, L.M., Feldheim, V., Deramaix, D.: Data driven prediction models of energy use of appliances in a low-energy house. In: Energy and Buildings, vol. 140, pp. 81–97, 1 April 2017. ISSN 0378-7788
Friedman, J.H.: Greedy function approximation: a gradient boosting. machine. Ann. Stat. 29(5), 1189–1232 (2001)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). PMID 9377276, doi:10.1162/neco.1997.9.8.1735
Acknowledgments
This work was supported by the Ministry of Education and Science of the Russian Federation in the framework of the state order “Organisation of Scientific Research”, task #2.6113.2017/6.7, and by grant of RFBR # 16-07-00625, supported by Russian President’s fellowship.
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Kholod, I., Efimova, M., Rukavitsyn, A., Andrey, S. (2017). Time Series Distributed Analysis in IoT with ETL and Data Mining Technologies. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NsCC NEW2AN 2017 2017 2017. Lecture Notes in Computer Science(), vol 10531. Springer, Cham. https://doi.org/10.1007/978-3-319-67380-6_9
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DOI: https://doi.org/10.1007/978-3-319-67380-6_9
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