• DocumentCode
    982563
  • Title

    Clustering of LDAP directory schemas to facilitate information resources interoperability across organizations

  • Author

    Liang, Jianghua ; Vaishnavi, Vijay K. ; Vandenberg, Art

  • Author_Institution
    Lexmark Int. Inc., Lexington, KY
  • Volume
    36
  • Issue
    4
  • fYear
    2006
  • fDate
    7/1/2006 12:00:00 AM
  • Firstpage
    631
  • Lastpage
    642
  • Abstract
    Directories provide a well-defined general mechanism for describing organizational resources such as the resources of the Internet2 higher education research community and the Grid community. Lightweight directory access protocol directory services enable data sharing by defining the information´s metadata (schema) and access protocol. Interoperability of directory information between organizations is increasingly important. Improved discovery of directory schemas across organizations, better presentation of their semantic meaning, and fast definition and adoption (reuse) of existing schemas promote interoperability of information resources in directories. This paper focuses on the discovery of related directory object class schemas and in particular on clustering schemas to facilitate discovering relationships and so enable reuse. The results of experiments exploring the use of self-organizing maps (SOMs) to cluster directory object classes at a level comparable to a set of human experts are presented. The results show that it is possible to discover the values of the parameters of the SOM algorithm so as to cluster directory metadata at a level comparable to human experts
  • Keywords
    access protocols; information resources; meta data; open systems; self-organising feature maps; clustering analysis; directory metadata; information resources interoperability; lightweight directory access protocol directory services; neural network configuration; self-organizing maps; Access protocols; Art; Authentication; Clustering algorithms; Humans; Information resources; Information systems; Internet; Middleware; Self organizing feature maps; Clustering analysis; LDAP directories; clustering evaluation; neural network configuration; self-organizing maps;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4427
  • Type

    jour

  • DOI
    10.1109/TSMCA.2005.851277
  • Filename
    1643814