• DocumentCode
    169882
  • Title

    Enterprise Architecture Intelligence: Combining Enterprise Architecture and Operational Data

  • Author

    Veneberg, R.K.M. ; Iacob, M.E. ; Van Sinderen, M.J. ; Bodenstaff, L.

  • Author_Institution
    Univ. of Twente, Enschede, Netherlands
  • fYear
    2014
  • fDate
    1-5 Sept. 2014
  • Firstpage
    22
  • Lastpage
    31
  • Abstract
    Combining enterprise architecture and operational data is complex (especially when considering the actual ´matching´ of data with enterprise architecture objects), and little has been written on how to do this. Therefore, in this paper we aim to fill this gap and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result either in an enriched enterprise architecture model (which is very suitable as a basis for model-based architecture analyses), or in a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data, and of business analytics results). The method is illustrated by means of a case study. Also, a model to store enterprise architecture, operational data and time is presented on which new forms of analysis may be performed.
  • Keywords
    business data processing; competitive intelligence; meta data; business analytics; enterprise architecture intelligence; enterprise architecture metadata; enterprise architecture model; model-based architecture; operational data; raw data interpretation; raw data retrieval; warehouse data model; Bismuth; Business; Computer architecture; Data models; Data visualization; Measurement; Servers; business intelligence; database; enterprise architecture; model; operational data; quantitative analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Enterprise Distributed Object Computing Conference (EDOC), 2014 IEEE 18th International
  • Conference_Location
    Ulm
  • ISSN
    1541-7719
  • Type

    conf

  • DOI
    10.1109/EDOC.2014.14
  • Filename
    6972047