skip to main content
10.5555/1151855.1151865dlproceedingsArticle/Chapter ViewAbstractPublication PagesapccmConference Proceedingsconference-collections
Article
Free Access

Defining and implementing domains with multiple types using mesodata modelling techniques

Authors Info & Claims
Published:01 January 2006Publication History

ABSTRACT

The integration of data from different sources often leads to the adoption of schemata that entail a loss of information in respect of one or more of the data sets being combined. The coercion of data to conform to the type of the unified attribute is one of the major reasons for this information loss. We argue that for maximal information retention it would be useful to be able to define attributes over domains capable of accommodating multiple types, that is, domains that potentially allow an attribute to take its values from more than one base type.Mesodata is a concept that provides an intermediate conceptual layer between the definition of a relational structure and that of attribute definition to aid the specification of complex domain structures within the database. Mesodata modelling techniques involve the use of data types and operations for common data structures defined in the mesodata layer to facilitate accurate modelling of complex data domains, so that any commonality between similar domains used for different purposes can be exploited.This paper shows how the mesodata concept can be extended to facilitate the creation of domains defined over multiple base types, and also allow the same set of base values to be used for domains with different semantics. Using an example domain containing values representing three different types of incomplete knowledge about the data item (coarse granularity, vague terms, or intervals) we show how operations and data structures for types already existing within the mesodata can simplify the task of developing a new intelligent domain.

References

  1. Abraham, T. & Roddick, J. F. (1999), 'Survey of spatio-temporal databases', GeoInformatica 3(1), 61-99.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Allen, J. (1983), 'Maintaining knowledge about temporal intervals', Communications of the ACM 26(11, November 1983), 832843.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Connolly, T. & Begg, C. (2005), Database Systems: A Practical Approach to Design, Implementation and Management, 4th Edition, Addison Wesley.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. de Vries, D., Rice, S. & Roddick, J. F. (2004), In support of mesodata in database management systems, in 'DEXA 2004', Springer, Zaragoza, Spain.]]Google ScholarGoogle Scholar
  5. de Vries, D. & Roddick, J. F. (2004), Facilitating database attribute domain evolution using meso-data, in F. Grandi, ed., 'Third International Workshop on Evolution and Change in Data Management (ECDM2004)', Lecture Notes in Computer Science, Springer, Shanghai.]]Google ScholarGoogle ScholarCross RefCross Ref
  6. Dey, D. & Sarkar, S. (1996), 'A probabilistic relational model and algebra', ACM Transactions on Database Systems 21(3), 339369.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Egenhofer, M. J. & Franzosa, R. D. (1991), 'Point-set topological spatial relations', International Journal for Geographical Information Systems 5(2), 161-174.]]Google ScholarGoogle ScholarCross RefCross Ref
  8. Lorentz, D. & Gregoire, J. (2003), Oracle Database SQL Reference 10g Release 1 (10.1), Oracle Corporation.]]Google ScholarGoogle Scholar
  9. Melton, J. & Simon, A. R. (2002), SQL:1999 - Understanding Relational Language Components, Morgan Kaufmann Publishers.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. MySQL (2003), 'SQL open source software'.]]Google ScholarGoogle Scholar
  11. Rice, S. & Roddick, J. F. (2000), Lattice-structured domains, imperfect data and inductive queries, in M. Ibrahim, J. Kung & N. Revell, eds, '11th International Conference on Database and Expert Systems Applications, DEXA 2000', Lecture Notes in Computer Science, Springer, London, pp. 664-674.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Roddick, J. (1994), A Model for Temporal Inductive Inference and Schema Evolution in Relational Database Systems, Doctor of philosophy, La Trobe University.]]Google ScholarGoogle Scholar
  13. Schneider, M. (1997), Spatial Data Types for Database Systems, Vol. 1288 of Lecture Notes in Computer Science, Springer.]]Google ScholarGoogle ScholarCross RefCross Ref
  14. Snodgrass, R. T. (1995), The TSQL2 Temporal Query Language, Kluwer Academic Publishers.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Swan, V. G. (1984), The pottery kilns of Roman Britain, Royal Commission for Historical Monuments.]]Google ScholarGoogle Scholar
  16. Vilain, M. B. (1982), A system for reasoning about time, in 'National Conference on Artificial Intelligence', Pittsburg, PA, pp. 197-201.]]Google ScholarGoogle Scholar
  17. Zeng, J. (1999), Research and practical experiences in the use of multiple data sources for enterprise-level planning and decision making: A literature review, Technical report, Center for Technology in Government, University at Albany / SUNY.]]Google ScholarGoogle Scholar

Index Terms

  1. Defining and implementing domains with multiple types using mesodata modelling techniques

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Article Metrics

              • Downloads (Last 12 months)8
              • Downloads (Last 6 weeks)1

              Other Metrics

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader