Integrating Heterogeneous Engineering Tools and Data Models: A Roadmap for Developing Engineering System Architecture Variants

  • Richard Mordinyi
  • Dietmar Winkler
  • Florian Waltersdorfer
  • Stefan Scheiber
  • Stefan Biffl
Conference paper

DOI: 10.1007/978-3-319-13251-8_6

Volume 200 of the book series Lecture Notes in Business Information Processing (LNBIP)
Cite this paper as:
Mordinyi R., Winkler D., Waltersdorfer F., Scheiber S., Biffl S. (2015) Integrating Heterogeneous Engineering Tools and Data Models: A Roadmap for Developing Engineering System Architecture Variants. In: Winkler D., Biffl S., Bergsmann J. (eds) Software Quality. Software and Systems Quality in Distributed and Mobile Environments. SWQD 2015. Lecture Notes in Business Information Processing, vol 200. Springer, Cham

Abstract

Developing large systems engineering projects require combined efforts of various engineering disciplines. Each engineering group uses specific engineering tools and data model concepts representing interfaces to other disciplines. However, individual concepts lack in completeness and include strong limitations regarding interoperability and data exchange capabilities. Thus, highly heterogeneous data models cause semantic gaps that hinder efficient collaboration between various disciplines. The design of an integration solution within a systematic engineering process typically requires re-modelling of the common data model (used for mapping individual local tool data models) to enable efficient data integration. However, designing and implementing integration approaches include continuously collecting new knowledge on the related application domains, in our case automation systems engineering projects, and integration capability that meet requirements of related domains. In this paper we report on a sequence of different architectural designs for an efficient and effective integration solution that lead to a similar and stable data model design for application in the automation systems domain. By means of iterative prototyping, candidates for modelling styles were tested for feasibility in context of industry use cases. In addition we applied an adjusted Architecture Tradeoff Analysis Method (ATAM) to assess the resulting final architecture variant.

Keywords

Semantic integration Data modelling Service design Service modelling 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Richard Mordinyi
    • 1
  • Dietmar Winkler
    • 1
  • Florian Waltersdorfer
    • 1
  • Stefan Scheiber
    • 1
  • Stefan Biffl
    • 1
  1. 1.Christian Doppler Laboratory, Software Engineering Integration for Flexible Automation SystemsVienna University of TechnologyViennaAustria