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Study on user customized data service model for improving data service reliability

Published:05 January 2017Publication History

ABSTRACT

New value was extracted through managing and analyzing huge volume of data in Big Data paradigm. With the paradigm, data environment such as Public Data and Data Market was built to collect and provide data for data users. Some issues to realize data-centric economy were naturally arisen. The Public Data or Data Market focused on an environment that data providers provide data to data customers. In other words, "Delivery" was focused. They had less consideration on "Use." For this reason, formats of provided data were different in each providers and ways of using data were also different. It was hard for data customers to use data. Data without applying data user's requirements occurred additional time and resource cost to use data. It was one factor to hinder the growth of data-centric economy.

Therefore, a reference model and an algorithm were proposed in this paper. The reference model included the aspect of "Use" by considering data user's requirement. In order to consider data user's requirement, the algorithm considering relationship between data volume and limited time was surely necessary. The algorithm would support to maximize data availability and usability. The algorithm was used inside of the reference model to support in-time factor to guarantee service reliability satisfying various user's requirements. The concept and details of reference model and algorithm would be explained in the main body of this paper.

Consequently, this paper could contribute for data customers to reduce additional computing and network resource usage because of providing data that is suitable for user's requirements. It might decrease battery and network consumption of mobile devices. In addition, Big Data analysis using this model might reduce processes of data collecting and preprocessing, and guarantee maximum data volume in limited time.

References

  1. Oracle, "Media for Long-Term Archiving", Oracle, 2014.Google ScholarGoogle Scholar
  2. Public Data Group, "The Public Data Group: supporting the National Information Infrastructure", Public Data Group, 2014.Google ScholarGoogle Scholar
  3. Anne Kauhanen-Simanainen, Margit Suurhasko, Mikael Vakkari, "The open data goals and action proposals 2015--2020", The Finnish Open Data Programme, 2015.Google ScholarGoogle Scholar
  4. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung, "The Google File System", Google, 2003.Google ScholarGoogle Scholar
  5. Kejiang Ye, Dawei Huang, Xiaohong Jiang, Huajun Chen, Shuang Wu, "Virtual machine Based Energy-Efficient Data Center Architecture for Cloud Computing: A Performance Perspective", 2010 IEEE/ACM International Conference on Green Computing and Communications & 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al., "Above the clouds: A berkeley view of cloud computing," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009--28, 2009.Google ScholarGoogle Scholar
  7. DMTF, "Software Defined Data Center (SDDC) Definition - A White Paper from the OSDDC Incubator", DMTF, 2015Google ScholarGoogle Scholar
  8. Holger Relnhardt, "Why You Will need Data as a Service (DaaS)", ca technologies, 2013Google ScholarGoogle Scholar
  9. Tom Pringle, "Data-as-a-service: the Next Step in the As-a-service", OVUM, 2014.Google ScholarGoogle Scholar
  10. Gartner report, July 2009, available on line: (http://www.gartner.com/it/page.jsp?id=1064712)Google ScholarGoogle Scholar
  11. ISO/IEC JTC 1/SC 38, "ISO/IEC DIS 17788 (Information technology - Cloud Computing - Overview and Vocabulary) Text for Ballot", ISO, IEC, 2013Google ScholarGoogle Scholar
  12. ISO/IEC JTC 1/SC 38, "ISO/IEC DIS 17789 (Information technology - Cloud Computing - Reference Architecture) Text for Ballot", ISO, IEC, 2013Google ScholarGoogle Scholar
  13. Foued Jrad, Jie Tao and Achim Streit, "SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS", 2nd International Conference on Cloud Computing and Services Science, 2012.Google ScholarGoogle Scholar
  14. Ines Houidi, Marouen Mechtri, Wajdi Louati, Djamal Zeghlache, "Cloud Service Delivery Across Multiple Cloud Platforms", 2011 IEEE International Conference on Services Computing, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S.G. Grivas, T. Uttam Kumar, H. Wache, "Cloud Broker: Bringing Intelligence into the Cloud an Event-Based Approach". IEEE 3rd International Conference on Cloud Computing (CLOUD2010), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. K. Nair, et al., "Towards Secure Cloud Bursting, Brokerage, and Aggregation", 8th European Conference on Web Services, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Edith Ramirez, Julie Brill, Maureen K. Ohlhausen, Joshua D. Wright, Terrell McSweeny, "Data Brokers - A Call for Transparency and Accountability", Federal Trade Commision (FTC), 2014.Google ScholarGoogle Scholar
  18. Jane W.S. Liu, Kwei-Jay Lin, Wei-Kuan Shih, and Albert Chuang-shi Yu, "Algorithms for scheduling imprecise computations," IEEE Computer, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. JANE W. S. LIU, WEI-KUAN SHIH, KWEI-JAY LIN, RICCARDO BETTAT, "Imprecise Computations," IEEE, 1994.Google ScholarGoogle Scholar
  20. David Hull, Wu-chun Feng, and Jane W. S. Liu, "Operating System Support for Imprecise Computation," AAAI Fall Symposium on Flexible Computation, 1996.Google ScholarGoogle Scholar
  21. Leo Budin, Domagoj Jakobovic, Marin Golub, "Genetic Algorithms in Real-Time Imprecise Computing," Computing and Information Technology, 2000.Google ScholarGoogle Scholar
  22. M. Amirijoo, J. Hansson, and S.H. Son, "Algorithms for Managing QoS for Real-Time Data Services Using Imprecise Computation," Real-Time and Embedded Computing Systems and Applications (RTCSA), 2003.Google ScholarGoogle Scholar
  23. JIA-MING CHEN, WAN-CHEN LU, WEI-KUAN SHIH AND MING-CHUNG TANG, "Imprecise Computations with Deferred Optional Tasks," INFORMATION SCIENCE AND ENGINEERING 25, 2009.Google ScholarGoogle Scholar
  24. Tom Bradicich, Stephanie Orci, "The Moore's Law of Big Data", National Instruments, 2013.Google ScholarGoogle Scholar
  25. ITU-T SG13 Q17/13, "Requirements and capabilities for cloud computing based big data", ITU-T, 2015.Google ScholarGoogle Scholar
  26. HIBA JASIM HADI, AMMAR HAMEED SHNAIN, SARAH HADISHAHEED, AZIZAHBT HAJI AHMAD, "BIG DATA AND FIVE V'S CHARACTERISTICS", IRF International Conference, 2014Google ScholarGoogle Scholar
  27. Nrusimham Ammu, Mohd Irfanuddin, "Big Data Challenges", International Journal of Advanced Trends in Computer Science and Engineering, 2013Google ScholarGoogle Scholar
  28. Alexandros Labrinidis, H. V. Jagadish, "Challenges and Opportunities with Big Data", 2012 VLDB Endowment, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Nasser Thabet, Tariq Rahim Soomro, "Big Data Challenges", Journal of Computer Engineering & Information Technology, 2015.Google ScholarGoogle Scholar
  30. Milan Vojnovic, Fei Xu, Jingren Zhou, "Sampling Based Range Partition Methods for Big Data Analytics", Microsoft Technical Report, 2012.Google ScholarGoogle Scholar
  31. Meta S. Brown, "Big Data Blasphymy: Why Sample?", SmartData Collective, 2015.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication
      January 2017
      746 pages
      ISBN:9781450348881
      DOI:10.1145/3022227

      Copyright © 2017 ACM

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      New York, NY, United States

      Publication History

      • Published: 5 January 2017

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      IMCOM '17 Paper Acceptance Rate113of366submissions,31%Overall Acceptance Rate213of621submissions,34%

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