Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
권호 표지
DOI QR코드

DOI QR Code

Design of Customized Research Information Service Based on Prescriptive Analytics

처방적 분석 기반의 연구자 맞춤형 연구정보 서비스 설계

  • Lee, Jeong-Won (Department of Information Communication Engineering, Mokwon University) ;
  • Oh, Yong-Sun (Department of Information Communication Engineering, Mokwon University)
  • 이정원 (목원대학교 정보통신공학) ;
  • 오용선 (목원대학교 정보통신공학)
  • Received : 2022.03.29
  • Accepted : 2022.04.27
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Big data related analysis techniques, the prescriptive analytics methodology improves the performance of passive learning models by ensuring that active learning secures high-quality learning data. Prescriptive analytics is a performance maximizing process by enhancing the machine learning models and optimizing systems through active learning to secure high-quality learning data. It is the best subscription value analysis that constructs the expensive category data efficiently. To expand the value of data by collecting research field, research propensity, and research activity information, customized researcher through prescriptive analysis such as predicting the situation at the time of execution after data pre-processing, deriving viable alternatives, and examining the validity of alternatives according to changes in the situation Provides research information service.

빅데이터 관련 분석 기법에서 처방적 분석 방법론은 적극적인 학습이 양질의 학습 데이터를 확보함으로써 수동적인 학습모델의 성능을 개선하고, 해당 시스템을 최적화하여 성능의 극대화를 통해 처리 프로세싱 과정을 다루며 판단의 근거가 되는 이유를 제시하고 있다. 그리고 범주 정보가 없는 데이터의 경우 기계가 이를 분석하여 애매한 것과 경계지점에 놓인 것들을 찾아내 수동으로 판단하게 하여 값비싼 범주 데이터를 매우 효과적으로 구축하는 방식이다. 연구자 역량을 강화하기 위하여 연구자의 연구 분야, 연구 성향, 연구 활동정보 등을 수집하여 데이터가 가진 가치를 확장하기 위해 데이터 전처리 후 실행 시점의 상황 예측하고 실행 가능한 대안 도출을 통해 상황 변동에 따른 대안 유효성 검토 등 처방적 분석을 통하여 연구자 맞춤형 연구정보 서비스를 제공한다.

Keywords

References

  1. Douglas Laney et al., "Gartner Predicts 2013: Information Innovation", Gartner Inc., 2013.
  2. Frazzetto, Davide, et al., "Prescriptive Analytics: A Survey of Emerging Trends and Technologies." The VLDB Journal, Vol.28, No.4, pp.575-595, 2019. https://doi.org/10.1007/s00778-019-00539-y
  3. WN. Sadat Mosavi and M. Filipe Santos, "How Prescriptive Analytics Influences Decision Making in Precision Medicine," Procedia Computer Science, Vol.177, pp.528-533, 2020. https://doi.org/10.1016/j.procs.2020.10.073
  4. Santiago, A. M., and Smith, R. J., "What can "Big data" methods offer human services research on organizations and communities?" Human Service Organizations: Management, Leadership & Governance, Vol.43, No.4, pp.344-356, 2019. https://doi.org/10.1080/23303131.2019.1674756
  5. Riabacke, Mona; Danielson, Mats; Ekenberg, Love., "State-of-the-Art Prescriptive Criteria Weight Elicitation". Advances in Decision Sciences, Vol.2012, pp.1-24, 2012. https://doi.org/10.1155/2012/276584
  6. Lepenioti, K., Bousdekis, A., Apostolou, D., and Mentzas, G., "Prescriptive analytics: Lite-rature review and research challenges". International Journal of Information Management, Vol.50, pp.57-70, 2020. https://doi.org/10.1016/j.ijinfomgt.2019.04.003
  7. Frazzetto, Davide Nielsen, Thomas Dyhre Pedersen, Torben Bach Siksnys, Laurynas., "Prescriptive analytics: A survey of emerging trends and technologies". The VLDB Journal, Vol.28, No.4, pp.575-595, 2019. https://doi.org/10.1007/s00778-019-00539-y
  8. Steenstrup, K.; Sallam, R. L.; Eriksen, L.; Jacobson, S. F., "Industrial Analytics Revolutionizes Big Data in the Digital Business". Gartner Research, 2014.
  9. Hupfeld, D., Maccioni, R., Sesemann, R., Ravazzolo, D., "Fleet asset capacity analysis and revenue management optimization using advanced prescriptive analytics". J. Revenue Pricing Manag, Vol.15, No.6, pp.516-522, 2016. https://doi.org/10.1057/s41272-016-0064-y
  10. Soltanpoor, R., Sellis, T., Prescriptive analytics for big data. In: Databases Theory and Applications-27th Australasian Database Conference, pp.245-256, 2016.
  11. Wu, P.J., Yang, C.K., The green fleet optimization model for a low-carbon economy: A prescriptive analytics. ICASI, pp.107-110, 2017.
  12. Bill Vorhies, "Prescriptive versus Predictive Analytics-A Distinction without a Difference?". Predictive Analytics Times, 2014.
  13. Prescriptive analytics. Wikipedia[Internet], https://en.wikipedia.org/wiki/Prescriptive_analytics.
  14. L.-J. Lee, & S.-J. Kim, A Study on the Information Use Behaviors of Researchers in the Field of Business Administration for Improving Information Services. Journal of the Korean BIBLIA Society for Library and Information Science, Vol.26, No.1, pp.279-302, 2015. https://doi.org/10.14699/KBIBLIA.2015.26.1.279
  15. Jeong-Hwan. Kim, Jay-Hoon Kim and Jae-Young Hwang, "A Study on Information Users' Needs and Information Seeking Behavior of Doctoral Researchers in Digital Age." Journal of Korean Library and Information Society, Vol.42, No.3, pp.189-208, 2011. https://doi.org/10.16981/kliss.42.3.201109.189
  16. Lestari, F., Herman, T., & Sujana, A., Using The 5W1H Method in Writing Important Information with Google Forms in Elementary Schools. The 3rd International Conference on Elementary Education, Vol.3, No.1, pp.207-211, 2021.