Cluster Computing

, Volume 22, Issue 4, pp 1039–1048 | Cite as

Sequential-index pattern mining for lifecare telecommunication platform

  • Joo-Chang Kim
  • Kyungyong ChungEmail author


Lifecare for quality-of-life improvement includes not only the prevention and treatment of diseases and health management anywhere and at any-time, but also the improvement of lifestyle, the psychological environment, and the emergency response. The construction of a sustainable model on the telecommunications platform as a solution has been constantly studied using intelligence information technology. In this study, sequential-index pattern mining on the lifecare telecommunications platform is proposed. The method is as follows. Various temporal and regional indices that are provided by the Meteorological Administration, Meteorological Data Open Portal, Public Data Portal, Healthcare Big Data Hub, and Korea Environment Corporation are collected as lifecare index data. The collected index data are transmitted to the NAS file server of the lifecare telecommunications platform using peer-to-peer (P2P)-based interconnection technology and high-performance computing. The sequential relationships among the indices are discovered to consider the semantic relationships. Using the AprioriAll mining algorithm, the maximum sequence is determined to find sequential relationships in the frequent sequence set. To process the pattern extraction and computation efficiently, recomposed transactions are stored in the high-performance NAS file server. Also, a mining-index prediction model is developed in consideration of the sequential relationships to complement the weakness of the statistical time-series analysis. Since the regional meteorological conditions are similar, the index sequential pattern can solve the problem of the generation of omissions or errors regarding the real-time monitoring data that are due to the monitoring equipment or communication failures. Therefore, the proposed sequential-index pattern mining can be utilized as a lifecare tool for risk-factor detection, risk prediction, and trend analysis.


Sequential pattern Data mining Lifecare Telecommunication platform 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2016R1D1A1A09917313)


  1. 1.
    Chung, W.Y., Lee, Y.D., Jung, S.J.: A wireless sensor network compatible wearable U-healthcare monitoring system using integrated ECG, accelerometer and SpO2. In: Proc. of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1529–1532 (2008)Google Scholar
  2. 2.
    Jung, E.J., Kim, J.C., Jung, H., Yoo, H., Chung, K.: Mining based mental health and blood pressure management service for smart health. J Korea Converg Soc 8(1), 13–18 (2017)CrossRefGoogle Scholar
  3. 3.
    Jung, H., Chung, K.: Knowledge-based dietary nutrition recommendation for obese management. Inf. Technol. Manag. 17(1), 29–42 (2016)CrossRefGoogle Scholar
  4. 4.
    Song, C.W., Jung, H., Chung, K.: Development of a medical big-data mining process using topic modeling. Clust. Comput. (2017). CrossRefGoogle Scholar
  5. 5.
    Jung, H., Chung, K.: Life style improvement mobile service for high risk chronic disease based on PHR platform. Clust. Comput. 19(2), 967–977 (2016)CrossRefGoogle Scholar
  6. 6.
    Park, M., Yoon, E., Lim, Y.H., Kim, H., Choi, J., Yoon, H.J.: Renal hyper filtration as a novel marker of all-cause mortality. J Am Soc Nephrol 26(6), 1426–1433 (2015)CrossRefGoogle Scholar
  7. 7.
    Rho, M.J., Kim, H.S., Chung, K., Choi, I.Y.: Factors influencing the acceptance of telemedicine for diabetes management. Clust. Comput. 18(1), 321–331 (2015)CrossRefGoogle Scholar
  8. 8.
    Rho, M.J., Jang, K.S., Chung, K., Choi, I.Y.: Comparison of knowledge, attitudes, and trust for the use of personal health information in clinical research. Multimed. Tools Appl. 74(7), 2391–2404 (2015)CrossRefGoogle Scholar
  9. 9.
    Chung, K., Kim, J.C., Park, R.C.: Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P. Inf. Technol. Manag. 17(1), 67–80 (2016)CrossRefGoogle Scholar
  10. 10.
    Hashem, I.A., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)CrossRefGoogle Scholar
  11. 11.
    Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)CrossRefGoogle Scholar
  12. 12.
    Chung, K., Park, R.C.: Chatbot-based heathcare service with a knowledge base for cloud computing. Clust. Comput. (2018). CrossRefGoogle Scholar
  13. 13.
    Jeon, B.K., Choi, Y.K.: Distributed and parallel processing: design and implementation of an efficient migration policy for mobile agents. Inf. Process. Soc. J. 6(7), 1770–1776 (1999)Google Scholar
  14. 14.
    Korea Meteorological Administration.
  15. 15.
    Weather data open portal.
  16. 16.
    Open data Portal.
  17. 17.
    Health Insurance Review and Assessment Service (HIRA).
  18. 18.
    Korea Environment Corporation.
  19. 19.
  20. 20.
    National Ambient Air Information System.
  21. 21.
    Jung, H., Chung, K.: Sequential pattern profiling based bio-detection for smart health service. Clust. Comput. 18(1), 209–219 (2015)CrossRefGoogle Scholar
  22. 22.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of 20th International Conference on Very Large Data Base, pp. 487–499 (1994)Google Scholar
  23. 23.
  24. 24.
    Kim, C., Lee, D.: Data mining technique for time series analysis of traffic data. Inst. Electron. Inf. Eng. 24(1), 59–62 (2001)Google Scholar
  25. 25.
    Ghemawat, S., Gobioff, H., Leung, S.T.: The Google File System. ACM SIGOPS Oper. Syst. Rev. 37(5), 29–43 (2003)CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Kim, J.C., Chung, K.: Depression index service using knowledge based crowdsourcing in smart health. Wirel. Pers. Commun. 93(1), 255–268 (2017)CrossRefGoogle Scholar
  28. 28.
    Kim, J.C., Chung, K.: Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Netw. Appl. 11(6), 1278–1287 (2018)CrossRefGoogle Scholar
  29. 29.
    Yoo, H., Chung, K.: Mining-based lifecare recommendation using peer-to-peer dataset and adaptive decision feedback. Peer-to-Peer Netw. Appl. 11(6), 1309–1320 (2018)CrossRefGoogle Scholar
  30. 30.
    HL7. Health Level Seven International.
  31. 31.
    Yoo, H., Chung, K.: PHR based diabetes index service model using life behavior analysis. Wirel. Pers. Commun. 93(1), 161–174 (2017)CrossRefGoogle Scholar
  32. 32.
    Chung, K., Park, R.C.: PHR open platform based smart health service using distributed object group framework. Clust. Comput. 19(1), 505–517 (2016)CrossRefGoogle Scholar
  33. 33.
    Yoo, H., Chung, K.: Heart rate variability based stress index service model using bio-sensor. Clust. Comput. (2017). CrossRefGoogle Scholar
  34. 34.
    Kim, J.C., Jung, H., Chung, K.: Mining based urban climate disaster index service according to potential risk. Wirel. Pers. Commun. 89(3), 1009–1025 (2016)CrossRefGoogle Scholar
  35. 35.
    Jung, H., Yoo, H., Chung, K.: Associative context mining for ontology-driven hidden knowledge discovery. Clust. Comput. 19(4), 2261–2271 (2016)CrossRefGoogle Scholar
  36. 36.
    Chung, K., Yoo, H., Choe, D.E.: Ambient context-based modeling for health risk assessment using deep neural network. J. Ambient Intell. Humaniz. Comput. (2018). CrossRefGoogle Scholar
  37. 37.
    Kim, J.C., Chung, K.: Neural-network based adaptive context prediction model for ambient intelligence. J. Ambient Intell. Humaniz. Comput. (2018). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Data Mining Lab, Department of Computer ScienceKyonggi UniversitySuwon-siSouth Korea
  2. 2.Division of Computer Science and EngineeringKyonggi UniversitySuwon-siSouth Korea

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