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A Conceptual Modeling Framework for Expressing Observational Data Semantics

  • Shawn Bowers
  • Joshua S. Madin
  • Mark P. Schildhauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5231)

Abstract

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.

Keywords

Observational Data Description Logic Cardinality Constraint Context Relationship Observation Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Andelman, S., Bowles, C., Willig, M., Waide, R.: Understanding environmental complexity through a distributed knowledge network. BioSciences 54(3), 240–246 (2004)CrossRefGoogle Scholar
  2. 2.
    Ellison, A., et al.: Analytic webs support the synthesis of ecological datasets. Ecology 87, 1345–1358 (2006)CrossRefGoogle Scholar
  3. 3.
    Madin, J., Bowers, S., Schildhauer, M., Jones, M.: Advancing ecological research with ontologies. Trends Ecol. Evol. 23(3), 159–168 (2008)CrossRefGoogle Scholar
  4. 4.
    Cox, S.: Observations and measurements. Technical Report 05-087r4, OGC (2006)Google Scholar
  5. 5.
    Tarboton, D., Horsburgh, J., Maidment, D.: CUAHSI community observations data model (ODM), version 1.0 (2007), http://water.usu.edu/cuahsi/odm/
  6. 6.
    Cushing, J., Nadkarni, N., Finch, M., Fiala, A., Murphy-Hill, E., Delcambre, L., Maier, D.: Component-based end-user database design for ecologists. J. Intell. Inf. Syst. 29(1), 7–24 (2007)CrossRefGoogle Scholar
  7. 7.
    McGuinness, D., et al.: The virtual solar-terrestrial observatory: A deployed semantic web application case study for scientific research. In: AAAI (2007)Google Scholar
  8. 8.
    Williams, R., Martinez, N., Goldbeck, J.: Ontologies for ecoinformatics. J. of Web Semantics 4, 237–242 (2006)CrossRefGoogle Scholar
  9. 9.
    Raskin, R.: Enabling semantic interoperability for earth science data (2004), http://sweet.jpl.nasa.gov
  10. 10.
    Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., Villa, F.: An ontology for describing and synthesizing ecological observation data. Eco. Inf. 2, 279–296 (2006)CrossRefGoogle Scholar
  11. 11.
    Tu, S., Wang, R.: Modeling data quality and context through extension of the ER model. In: Workshop on Information Technologies and Systems (1993)Google Scholar
  12. 12.
    Henricksen, K., Indulska, J., McFadden, T.: Modelling context information with ORM. In: OTM Workshops (2005)Google Scholar
  13. 13.
    Gregersen, H., Jensen, C.: Temporal entity-relationship models – a survey. TKDE 11, 464–497 (1999)Google Scholar
  14. 14.
    Stevens, S.: On the theory of scales of measurement. Science 103, 677–680 (1946)CrossRefzbMATHGoogle Scholar
  15. 15.
    Lenz, H., Shoshani, A.: Summarizability in OLAP and statistical data bases. In: SSDBM (1997)Google Scholar
  16. 16.
    McCarthy, J.: Notes on formalizing context. In: IJCAI (1993)Google Scholar
  17. 17.
    Beeri, C., Levy, A., Rousset, M.: Rewriting queries using views in description logics. In: PODS (1997)Google Scholar
  18. 18.
    Hurtado, C., Mendelzon, A.: OLAP dimension constraints. In: PODS (2002)Google Scholar
  19. 19.
    Guha, R., McCarthy, J.: Varieties of contexts. In: International and Interdisciplinary Conference on Modeling and Using Context (2003)Google Scholar
  20. 20.
    Analyti, A., Theodorakis, M., Spyratos, N., Constantopoulos, P.: Contextualization as an independent abstraction mechanism for conceptual modeling. Inf. Syst. 32(1), 24–60 (2007)CrossRefGoogle Scholar
  21. 21.
    Petit, J., Toumani, F., Boulicaut, J., Kouloumdjian, J.: Towards the reverse engineering of denormalized relational databases. In: ICDE (1996)Google Scholar
  22. 22.
    Alhajj, R.: Extracting the extended entity-relationship model from a legacy relational database. Inf. Syst. 28(6), 597–618 (2003)CrossRefzbMATHGoogle Scholar
  23. 23.
    Davis, K., Aiken, P.: Data reverse engineering: A historical survey. In: WCRE (2000)Google Scholar
  24. 24.
    An, Y., Borgida, A., Mylopoulos, J.: Discovering the semantics of relational tables through mappings. J. Data Semantics VII, 1–32 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shawn Bowers
    • 1
  • Joshua S. Madin
    • 2
  • Mark P. Schildhauer
    • 3
  1. 1.Genome CenterUniversity of CaliforniaDavis
  2. 2.Dept. of Biological SciencesMacquarie UniversityAustralia
  3. 3.National Center for Ecological Analysis and Synthesis, UC Santa BarbaraUSA

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