OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

On the Move to Meaningful Internet Systems: OTM 2015 Conferences pp 267-284 | Cite as

Determining the Quality of Product Data Integration

  • Julian Tiedeken
  • Thomas Bauer
  • Joachim Herbst
  • Manfred Reichert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9415)


To meet customer demands, companies must manage numerous variants and versions of their products. Since product-related data (e.g., requirements’ specifications, geometric models, and source code, or test cases) are usually scattered over a large number of heterogeneous, autonomous information systems, their integration becomes crucial when developing complex products on one hand and aiming at reduced development costs on the other. In general, product data are created in different stages of the product development process. Furthermore, they should be integrated in a complete and consistent way at certain milestones during process development (e.g., prototype construction). Usually, this data integration process is accomplished manually, which is both costly and error prone. Instead semi-automated product data integration is required meeting the data quality requirements of the various stages during product development. In turn, this necessitates a close monitoring of the progress of the data integration process based on proper metrics. Contemporary approaches solely focus on metrics assessing schema integration, while not measuring the quality and progress of data integration. This paper elicits fundamental requirements relevant in this context. Based on them, we develop appropriate metrics for measuring product data quality and apply them in a case study we conducted at an automotive original equipment manufacturer.


Product data integration Integration quality Integration Process 


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  1. 1.
    Easterbrook, S., Finkelstein, A., Kramer, J., Nuseibeh, B.: Coordinating Distributed ViewPoints: the anatomy of a consistency check. CERA 2(3), 209–222 (1994)Google Scholar
  2. 2.
    Wache, H., et al.: Ontology-based integration of information - A survey of existing approaches. In: Proc. IJCAI 2001 Workshop, pp. 108–117 (2001)Google Scholar
  3. 3.
    Philips, L.: Hanging on the Metaphone. Computer Language 7(12), 39–44 (1990)Google Scholar
  4. 4.
    Stark, J.: Product Lifecycle Management. Springer (2011)Google Scholar
  5. 5.
    Wiederhold, G., Qian, X.: Consistency control of replicated data in federated databases. In: Workshop on the Management of Replicated Data, pp. 130–132 (1990)Google Scholar
  6. 6.
    Sheth, A.P., Rusinkiewicz, M.: Management of interdependent data: specifying dependency and consistency requirements. In: Workshop on the Management of Replicated Data, pp. 133–136 (1990)Google Scholar
  7. 7.
    Wiederhold, G., Qian, X.: Modeling asynchrony in distributed databases. In: Proc. ICDE 1987, pp. 246–250 (1987)Google Scholar
  8. 8.
    Tiedeken, J., Reichert, M., Herbst, J.: On the Integration of Electrical/Electronic Product Data in the Automotive Domain. Datenbank Spektrum 13(3), 189–199 (2013)CrossRefGoogle Scholar
  9. 9.
    Batista, M.D.C.M., Salgado, A.C.: Information quality measurement in data integration schemas. In: Proc. QDB 2007, pp. 61–72 (2007)Google Scholar
  10. 10.
    Herzog, T.N., Scheuren, F.J., Winkler, W.E.: Data Quality and Record Linkage Techniques. Springer (2007)Google Scholar
  11. 11.
    Wang, J.: A quality framework for data integration. In: MacKinnon, L.M. (ed.) BNCOD 2010. LNCS, vol. 6121, pp. 131–134. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  12. 12.
    Duchateau, F., Bellahsene, Z.: Measuring the quality of an integrated schema. In: Proc. ER 2010, pp. 261–273 (2010)Google Scholar
  13. 13.
    Roland Berger Strategy Consultants. Mastering Product Complexity, Düsseldorf, November 2012Google Scholar
  14. 14.
    Wang, R.Y., Strong, D.M.: Beyond Accuracy: What Data Quality Means to Data Consumers. J. of Management Information Systems 12(4), 5–33 (1996)MATHGoogle Scholar
  15. 15.
    Gennari, J.H., et al.: The evolution of Protégé: an environment for knowledge-based systems development. Int. J. Hum.-Comput. Stud. 58(1), 89–123 (2003)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Motik, B., et al.: OWL 2 Web Ontology Language: Structural Specification and Functional-Style Syntax. W3C recommendation 27.65 (2009)Google Scholar
  17. 17.
    Horrocks, I., et al.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML. W3C Member Submission, May 21, 2004Google Scholar
  18. 18.
    Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL Ontologies. Semantic Web 2(1), 11–21 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julian Tiedeken
    • 1
  • Thomas Bauer
    • 2
  • Joachim Herbst
    • 3
  • Manfred Reichert
    • 1
  1. 1.Institute of Databases and Information SystemsUlm UniversityUlmGermany
  2. 2.Department Information ManagementUniversity of Applied SciencesNeu-UlmGermany
  3. 3.ITM Group Research & Product Development MBCDaimler AGBöblingenGermany

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