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Big data and rule-based recommendation system in Internet of Things

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Abstract

This paper proposes a recommendation system based on big data framework and rule-based system in the era of Internet of Things. With the emergence of the smart devices beginning from smart phones extends to the general electronic devices such as smart tv sets, refrigerators, washing machines, robot vacuums, and so on. Such smart devices make it possible to collect the device-usage logs of end users whereby a system is able to analyze it to find the usage patterns of the end users and make recommendations to the end users. Furthermore, this allows to make recommendations on the individual users since the smart devices have their own identifiers such as MAC address and IPv6 address. The smart devices also have matched information with the end user id/s. In this study, we propose a method for analyzing the devise-usage patterns in semi-real time based on the big-data system architecture. We also present a recommendation framework which makes device-usage recommendations by using a rule-based system architecture with the analyzed usage patterns. Lastly, we introduce a segmentation-based analysis and recommendation framework to make recommendations based not only on his or her own usage patterns, but also on the common usage patterns of the users who are living in a similar context. The segmentation is formed also based on the types of the device usages, so that the analysis can be performed in a batch process thereby enabling to make the recommendations in real time based on the pre-analyzed usage patterns.

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References

  1. Buchanan, B.G.: Rule-Based Expert Systems, vol. 3. Addison-Wesley, Reading (1984)

    Google Scholar 

  2. Abraham, A.: Rule-Based Expert Systems. Handbook of Measuring System Design. Wiley, Oklahoma (2005)

    Google Scholar 

  3. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  4. Ho, Y.H., Kim, J.K., Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Syst. Appl. 23(3), 329–342 (2002)

    Article  Google Scholar 

  5. Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agents Multi Agent Syst. 16(1), 57–74 (2008)

    Article  Google Scholar 

  6. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd International Workshop on Web Information and Data Management, pp. 9-15. ACM, New York (2001)

  7. Adomavicius, G., Tuzhilin, A.: Expert-driven validation of rule-based user models in personalization applications. Data Min. Knowl. Discov. 5(1), 33–58 (2001)

    Article  MATH  Google Scholar 

  8. Apache Hadoop. https://hadoop.apache.org/

  9. Apache MapReduce. https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html

  10. IFTTT. https://ifttt.com/

  11. Gupta, B.: Real-time context aware recommendation engine based on a user internet of things environment. US patent application no. 14/324,917 (2014)

  12. Barricelli, B.R., Valtolina, S.: Designing for End-User Development in the Internet of Things. In: End-user development, pp. 9-24. Springer, New York (2015)

  13. Mashal, I., Chung, T.Y., Alsaryrah, O.: Toward service recommendation in Internet of Things. In: Seventh International Conference on Ubiquitous and Future Networks (ICUFN), pp. 328–331. IEEE, Berlin (2015)

  14. Evans, D.: The Internet of Things: how the next evolution of the internet is changing everything. CISCO White Pap. 1, 1–11 (2011)

    Google Scholar 

  15. Apache HBase. https://hbase.apache.org/

  16. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D.: Big data. The management revolution. Harv. Bus. Rev. 90(10), 61–67 (2012)

    Google Scholar 

  17. Kitchin, R.: The real-time city? Big data and smart urbanism. GeoJournal 79(1), 1–14 (2014)

    Article  Google Scholar 

  18. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications Co., Greenwich (2015)

    Google Scholar 

  19. Apache Hive. https://hive.apache.org/

  20. Chen, M., Shiwen, M., Yunhao, L.: Big data: a survey. Mobile Netw. Appl. 19(2), 171–209 (2014)

  21. Douglas, L.: 3D Data Management: Controlling Data Volume, Velocity and Variety. Gartner, Stamford (2001)

    Google Scholar 

  22. Xu, Z., Mei, L., Hu, C., Liu, Y.: The big data analytics and applications of the surveillance system using video structured description technology. Cluster Comput. 19(3), 1283–1292 (2016)

    Article  Google Scholar 

  23. Stonebraker, M.: SQL databases v. NoSQL databases. Commun. ACM 53(4), 10–11 (2010)

    Article  Google Scholar 

  24. Pokorny, J.: NoSQL databases: a step to database scalability in web environment. Int. J. Web Inf. Syst. 9(1), 69–82 (2013)

    Article  Google Scholar 

  25. Hirzel, M., et al.: IBM streams processing language: analyzing big data in motion. IBM J. Res. Dev. 57(3/4), 1–7 (2013)

    Article  Google Scholar 

  26. Kambatla, K., Kollias, G., Kumar, V., Grama, A.: Trends in big data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)

    Article  Google Scholar 

  27. Sakr, S.: Cloud-hosted databases: technologies, challenges and opportunities. Cluster Comput. 17(2), 487–502 (2014)

    Article  Google Scholar 

  28. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  29. Lämmel, R.: Google’s MapReduce programming model—revisited. Sci. Comput. Progr. 70(1), 1–30 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  30. Han, L., Ong, H.Y.: Parallel data intensive applications using MapReduce: a data mining case study in biomedical sciences. Cluster Comput. 18(1), 403–418 (2015)

  31. Jin, S., Lin, W., Yin, H., Yang, S., Li, A., Deng, B.: Community structure mining in big data social media networks with MapReduce. Cluster Comput. 18(3), 999–1010 (2015)

    Article  Google Scholar 

  32. Song, K., Lu, H.: High-performance XML modeling of parallel queries based on MapReduce framework. Cluster Comput. 19(4), 1975–1986 (2016)

    Article  Google Scholar 

  33. Hoy, M.B.: If this then that: an introduction to automated task services. Med. Ref. Serv. Quart. 34(1), 98–103 (2015)

    Article  Google Scholar 

  34. Vorapojpisut, S.: A lightweight framework of home automation systems based on the IFTTT model. JSW 10(12), 1343–1350 (2015)

    Article  Google Scholar 

  35. Blase, U.R., et al.: Trigger-action programming in the wild: an analysis of 200,000 IFTTT recipes. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 3227–3231. ACM, New York (2016)

  36. Gulwani, S., et al.: Inductive programming meets the real world. Commun. ACM 58(11), 90–99 (2015)

    Article  Google Scholar 

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Acknowledgements

This research was supported by Maximize the Value of National Science and Technology by Strengthen Sharing/Collaboration of National R&D Information funded by the Korea Institute of Science and Technology Information (KISTI).

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Authors and Affiliations

  1. NTIS Center, Korea Institute of Science and Technology Information, 245, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea

    Hanjo Jeong, Minwoo Park & Kiseok Choi

  2. Department of Business Statistics, Hannam University, 70 Hannamro, Daedeok-gu, Daejeon, Republic of Korea

    Byeonghwa Park

  3. Department of Computer & Information, Daejeon Health Institute of Technology, 21, Chungjeong-ro, Dong-gu, Daejeon, Republic of Korea

    Ki-Bong Kim

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  1. Hanjo Jeong
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Correspondence to Kiseok Choi.

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Jeong, H., Park, B., Park, M. et al. Big data and rule-based recommendation system in Internet of Things. Cluster Comput 22 (Suppl 1), 1837–1846 (2019). https://doi.org/10.1007/s10586-017-1078-y

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