Design of Convergence Platform for companion animal Personalized Services

반려동물 개인화서비스를 위한 융합 플랫폼 설계

  • Received : 2016.10.17
  • Accepted : 2016.12.20
  • Published : 2016.12.31


Nowadays, real-time devices that provide health care for a companion animal is being developed by IoT technology and its demand such as smart puppy tag is increasing. However, it is difficult for IoT devices of companion animals to process complex nature due to miniaturized hardware and constructive nature. There is a clear limit to custom advanced features like health care implementation. This paper designs an integrated platform with statistical analysis which makes it possible to customized services such as feed production, pharmaceutical production, and health care for each companion animal. Middleware that collects sensor information, customer's spending pattern and information from Social Network Service is also designed by making use of IoT devices which companion animals wear. Furthermore, the paper designed data analyzer which analyzes and refines data from collected information that can be applied to personalized services.


Recommending System;IoT;data acquisition;Data Preprocessor;Data analysis


  1. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Analysis of Recommendation Algorithms for ECommerce," Proc. of ACM EC '00 conference, pp.158-167, 2000.
  2. G. Adomavicius, and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. on Knowledge and Data Engineering, vol.17, no.6, pp.734-749, 2005.
  3. G. Karypis, "Evaluation of Item-Based Top-N Recommendation Algorithms," Proc. of CIKM '01 Conference, pp.247-254, 2001.
  4. A. Gosh and S. K. Das. A Distributed Greedy Algorithm for Connected Sensor Cover in Dense Sensor Networks. In Proceedings of Int'l Conference on Distributed Computing in Sensor Networks (DCOSS), 2005
  5. J. Horey, E. Begoli, R. Gunasekaran, S. Lim, and J. Nutaro, "Big Data Platforms as a Service: Challenges and Approach," USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2012.
  6. S. Koo and M. Shin, "A Study on the Enhancement Process of the Telecommunication Network Management using Big Data Analysis," Journal of the Korea Academia-Industrial cooperation Society, vol.13 no.12, pp.6060-6070, 2012.
  7. Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y. & Sun, X. "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature." Decision Support Systems 50, pp.559-569, 2010.
  8. M. Bilenko and M. Richardson, "Predictive client-side profiles for personalized advertisingg," in Proceedings of the 2011 ACM Conference on Knowledge Discovery and Data Mining, August 2011. Knowledge Discovery and Data Mining, August 2011.
  9. Ruotsalainen, Laura, Data Mining Tools for Techn ology and Competitive Intelligence, ESPOO 2008.
  10. Minsu Jang, Joo-chan Sohn, "Bossam: an extendedrule engine for the web," Proceedings of RuleML2004 (LNCS Vol. 3323), 2004.
  11. Surprenant, C. F. & Solomon, M. R. (1987). Predictability and personalization in the service encounter. Journal of Marketing, 51(2),86-96
  12. Open Web Platform Milestone Achieved with HTML5 Recommendation,
  13. Su Hyeon Namn, Kyoo-Sung Noh, "A Study on the Effective Approaches to Big Data Planning", Journal of digital Convergence , Vol. 13, No. 1, pp.227-235, 2015.
  14. Kyoo-Sung Noh, "Convergence Analysis of Recognition and Influence on Bigdata in the e-Learning Field", Journal of digital Convergence , Vol. 13, No. 10, pp. 51-58, 2015.
  15. Hye-Jung Jung, "The Analysis of Data on the basis of Software Test Data", Journal of digital Convergence, Vol. 13, No. 10, pp. 1-7, 2015.