Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

User Targerting SaaS Application Mash-Up Service Framework using Complex-Context and Rule-Martix

복합 콘텍스트 및 Rule-Matrix를 활용한 사용자 맞춤형 SaaS 어플리케이션 연동 서비스 프레임워크

  • Received : 2017.10.04
  • Accepted : 2017.06.12
  • Published : 2017.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the development of cloud computing, internet technology and Internet of Things(IoT), most of applications are being smarter and changing from native application to SaaS (Software as a Service) application. New versatile SaaS applications are being released through various app portals (e.g. appstore, googleplay, T-Store, and so on). However, a user has a difficulty in searching, choosing an suitable application to him. It is also hard for him to know what functions of each SaaS application are useful. He wants to be recommended something inter-operated SaaS service according to his personality and his situation. Therefore, this paper presents a way of making mash-up of SaaS applications in order to provide the most convenient inter-operated SaaS service to user. This paper also presents SaaS Application Mash-up Framework (SAMF), complex context and rule matrix. The proposed SAMF is a main system that totally manage SaaS application mash-up service. Complex context and rule matrix are key components in order to recommend what SaaS applications are needed and how those SaaS applications are inter-operated. The SAMF collects complex contexts (User Description, Status Description, SaaS Service Description) in order to choose which SaaS applications are useful, analyze what functions to use, how to mash-up.

Keywords

References

  1. H.K. Kim and S. Choi, “A Study of Policy Fostering for Cloud Service Brokerage,” Journal of International Conference on Convergence Technology, Vol. 5, No. 1, pp. 386-387, 2015.
  2. Cloud Service Brokerage-a Quick Guide to Aggregation(2015), https://www.linkedin.com/pulse/cloud-service-brokerage-quick-guideaggregation-nielsen-marsh (accessed Jun., 23, 2016).
  3. Cloudmore, Cloudmore Vendor Integration Guide, Cloudmore 2015, V. 5.0, 2015.
  4. About IFTTT(2014), https://ifttt.com/wtf (accessed Jul., 27, 2016).
  5. Connect Your Apps and Automate Work-Flows(2014), https://zapier.com/ (accessed Jul., 28, 2016).
  6. J.J. Jung, J.B. Park, and M.J. Kim, "User Targeting SaaS Service Mash-Up Framework," Proceeding of The 26th Joint Conference on Communications and Information, pp. 45-48, 2016.
  7. IITP, The Proposal of Development of SaaS Aggregation Technology for Cloud Service Mashup, IITP R0113-15-0008, V.1.0, 2015.
  8. J.J. Jung and M.J. Kim, "User Targeting SaaS Application Mash-Up Service Framework," Proceeding of The 12th International Conference on Multimedia Information Technology and Application, pp. 209-210, 2016.
  9. S.I. Lee and S.Y. Lee, “A Recommendation System Using Context-Based Collaborative Filtering,” Journal of The Korean Institute of Intelligent Systems, Vol. 21, No. 2, pp. 224-229, 2011. https://doi.org/10.5391/JKIIS.2011.21.2.224
  10. C.M. Seoung, S.J. Hong, and K.S. Lim, “A Context-based Adaptive Multimedia Streaming Scheme in IoT Environments,” Jounal of Korea Multimedia Society, Vol. 19, No. 7, pp. 1168-1172, 2016.
  11. Y.H. Byun, K.J. Jong, and K.C. Jung, “Personalized Bookmark Search Word Recommenation System based on Tag Keyword using Collaborative Filtering,” Jounal of Korea Multimedia Society, Vol. 19, No. 7, pp. 1882-1884, 2016.
  12. S.W. Keum, J.W. Moon, T.B. Lim, and J.I. Park, “The Method of User Interface and IoT Contorl in Broadcasting Devices,” Journal of Special Edition of Broadcast and Media, Vol. 20, No. 1, pp. 54-63, 2015.