Fit for Purpose: Toward an Engineering Basis for Data Exchange Standards

  • Arnon Rosenthal
  • Len Seligman
  • M. David Allen
  • Adriane Chapman
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 144)


Data standards are a powerful, real-world tool for enterprise interoperability, yet there exists no rigorous methodology for selecting among alternative standards approaches. This paper is a first step toward creating a detailed engineering basis for choosing among standards approaches. We define a specific sub-problem within a community’s data sharing challenge, and focus on it in depth. We describe the major choices (kinds of standards) applied to that task, examining tradeoffs. We present characteristics of a data sharing community that one should consider in selecting a standards approach—such as relative power, motivation level, and technical sophistication of different participants—and illustrate with real-world examples. We then show that one can state simple decision rules (based on engineering experience) that system engineers without decades of data experience can apply. We also comment on the methodology used, extracting lessons (e.g., “negative rules are simpler”) that can be used in similar analyses on other issues.


Science basis for enterprise interoperability experience reports on interoperability solutions reference ontologies and mapping mechanisms model-to-model transformations 


  1. 1.
    Bernstein, P.A., Melnik, S., Petropoulos, M., Quix, C.: Industrial-Strength Schema Matching. SIGMOD Record 33, 38–43 (2004)CrossRefGoogle Scholar
  2. 2.
    Doan, A., Domingos, P., Halevy, A.Y.: Learning to Match the Schemas of Databases: A Multistrategy Approach. Machine Learning 50, 279–301 (2003)CrossRefGoogle Scholar
  3. 3.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data Exchange: Semantics and Query Answering. In: Calvanese, D., Lenzerini, M., Motwani, R. (eds.) ICDT 2003. LNCS, vol. 2572, pp. 207–224. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Folmer, E., Luttighuis, P.O., van Hillegersberg, J.: Do Semantic Standards Lack Quality? A survey among 34 semantic standards. Electronic Markets 21(2) (2011)Google Scholar
  5. 5.
    Helmer, K.G., Ambite, J.L., Ames, J., Ananthakrishnan, R., Burns, G., Chervenak, A.L., Foster, I., Liming, L., Keator, D., Macciardi, F., Madduri, R., Navarro, J.P., Potkin, S., Rosen, B., Ruffins, S., Schuler, R., Turner, J.A., Toga, A., Williams, C., Kesselman, C.: Enabling collaborative research using the Biomedical Informatics Research Network (BIRN). J. Am. Med. Inform. Assoc. (April 2011)Google Scholar
  6. 6.
    Markus, M.L., Steinfield, C.W., Wigand, R.T., Minton, G.: Industry-wide information systems standardization as collective action: the case of the U.S. residential mortgage industry. MIS Quarterly 30(1) (August 2006)Google Scholar
  7. 7.
    Miller, R., Hernández, M.A., Haas, L.M., Yan, L., Ho, C.T.H., Fagin, R., Popa, L.: The Clio Project: Managing Heterogeneity. SIGMOD Record 30, 78–83 (2001)CrossRefGoogle Scholar
  8. 8.
    Rahm, E., Bernstein, P.A.: A Survey of Approaches to Automatic Schema Matching. The VDLB Journal 10, 334–350 (2001)Google Scholar
  9. 9.
    Rosenthal, A., Seligman, L., Renner, S.: From Semantic Integration to Semantics Management: Case Studies and a Way Forward. ACM SIGMOD Record, Special Issue on Semantic Integration (December 2004)Google Scholar
  10. 10.
    Rosenthal, A., Seligman, L., Blaustein, B.: Beyond the Sandbox: How Integration Researchers Can Actually Help Integration. In: Workshop on Information Integration, October 26-27. University of Pennsylvania, Philadelphia (2006)Google Scholar
  11. 11.
    Sarma, A.D., Dong, X., Halevy, A.Y.: Bootstrapping pay-as-you-go data integration systems. In: SIGMOD Conference, pp. 861–874 (2008)Google Scholar
  12. 12.
    Shvaiko, P., Euzenat, J.: A Survey of Schema-Based Matching Approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Zhao, K., Xia, M., Shaw, M.J.: Vertical E-Business Standards and Standards Developing Organizations: A conceptual framework. Electronic Markets 15(4), 289–300 (2005)CrossRefGoogle Scholar
  14. 14.
    Zhu, H., Wu, H.: Quality of Data Standards: Framework and Illustration using XBRL Taxonomy and Instances. Electronic Markets 21(2), 129–139 (2011)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Arnon Rosenthal
    • 1
  • Len Seligman
    • 2
  • M. David Allen
    • 2
  • Adriane Chapman
    • 2
  1. 1.The MITRE CorporationBedfordUSA
  2. 2.The MITRE CorporationMcLeanUSA

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