A conceptual modeling framework for expressing observational data semantics

Shawn Bowers*, Joshua S. Madin, Mark P. Schildhauer

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    30 Citations (Scopus)


    Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines. Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are used, and the needs of existing analysis applications make it impractical to define "one-size-fits-all" schemas for most observational data sets. Instead, new approaches are needed to flexibly describe observational data for effective discovery and integration. In this paper, we present a generic conceptual-modeling framework for capturing the semantics of observational data. The framework extends standard conceptual modeling approaches with new constructs for describing observations and measurements. Key to the framework is the ability to describe observation context, including complex, nested context relationships. We describe our proposed modeling framework, focusing on context and its use in expressing observational data semantics.

    Original languageEnglish
    Title of host publicationConceptual Modeling - ER 2008
    Subtitle of host publication27th International Conference on Conceptual Modeling, Proceedings
    EditorsQing Li, Stefano Spaccapietra, Eric Yu, Antoni Olivé
    Place of PublicationBerlin, Heidelberg
    PublisherSpringer, Springer Nature
    Number of pages14
    Volume5231 LNCS
    ISBN (Electronic)9783540878773
    ISBN (Print)3540878769, 9783540878766
    Publication statusPublished - 2008
    Event27th International Conference on Conceptual Modeling, ER 2008 - Barcelona, Spain
    Duration: 20 Oct 200824 Oct 2008

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5231 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349


    Other27th International Conference on Conceptual Modeling, ER 2008




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