• DocumentCode
    720998
  • Title

    RAISE: A Whole Process Modeling Method for Unstructured Data Management

  • Author

    Ling Chen ; Jian Shao ; Zhou Yu ; Jianling Sun ; Fei Wu ; Yueting Zhuang

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
  • fYear
    2015
  • fDate
    20-22 April 2015
  • Firstpage
    9
  • Lastpage
    12
  • Abstract
    Nowadays, unstructured data, e.g., texts, images, and videos, is growing in an explosive speed with the development of Internet and social network. Due to the variety of unstructured data, it is strongly desirable to design a generalized model to represent all kinds of unstructured data and build a system to organize them effectively. In this paper, we first define a generalized data model to represent unstructured data. Above the data model, we further propose RAISE, a whole process modeling method including Repository, Analysis, Index, Search, and Environment. Furthermore, we design a SQL-like unstructured query language (UQL) for flexible accessing the RAISE model. We implement the proposed method in a distributed unstructured data management system named D-Ocean, which is scalable, reliable, and high-available.
  • Keywords
    SQL; data models; database indexing; distributed databases; D-Ocean; RAISE model; SQL-like unstructured query language; UQL; distributed unstructured data management system; generalized data model; process modeling method; repository-analysis-index-search-and-environment; unstructured data representation; Analytical models; Data models; Database languages; Distributed databases; Feature extraction; Indexes; Videos; unstructured data management; whole process modeling; unstructured query language; D-Ocean;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia Big Data (BigMM), 2015 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-8687-3
  • Type

    conf

  • DOI
    10.1109/BigMM.2015.90
  • Filename
    7153767