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Anchor Modeling

An Agile Modeling Technique Using the Sixth Normal Form for Structurally and Temporally Evolving Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5829))

Abstract

Maintaining and evolving data warehouses is a complex, error prone, and time consuming activity. The main reason for this state of affairs is that the environment of a data warehouse is in constant change, while the warehouse itself needs to provide a stable and consistent interface to information spanning extended periods of time. In this paper, we propose a modeling technique for data warehousing, called anchor modeling, that offers non-destructive extensibility mechanisms, thereby enabling robust and flexible management of changes in source systems. A key benefit of anchor modeling is that changes in a data warehouse environment only require extensions, not modifications, to the data warehouse. This ensures that existing data warehouse applications will remain unaffected by the evolution of the data warehouse, i.e. existing views and functions will not have to be modified as a result of changes in the warehouse model.

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References

  1. Artale, A., Franconi, E.: Reasoning with Enhanced Temporal Entity-Relationship Models. In: Proc. of the 10th Intl. Workshop on Database and Expert Systems Applications (1999)

    Google Scholar 

  2. Bebel, B., Eder, J., Koncilia, C., Morzy, T., Wrembel, R.: Creation and Management of Versions in Multiversion Data Warehouses. In: ACM Symposium on Applied Computing (2004)

    Google Scholar 

  3. Booch, G., Rumbaugh, J., Jacobson, J.: The Unified Modelling Language User Guide. Addison Wesley, Reading (1999)

    Google Scholar 

  4. Carver, A., Halpin, T.: Atomicity and Normalization. In: Thirteenth International Workshop on Exploring Modeling Methods in Systems Analysis and Design, EMMSAD (2008)

    Google Scholar 

  5. Chen, P.: The Entity Relationship Model - Toward a Unified View of Data. ACM Transactions on Database Systems 1(1), 9–36 (1976)

    Article  Google Scholar 

  6. Date, C.E., Darwen, H., Lorentzos, N.A.: Temporal Data and the Relational Model. Elsevier Science, Amsterdam (2003)

    Google Scholar 

  7. Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 5th edn. Addison-Wesley, Reading (2006)

    Google Scholar 

  8. Fowler, M.: Analysis Patterns: Reusable Object Models. Addison-Wesley, Reading (1997)

    Google Scholar 

  9. Gregersen, H., Jensen, J.S.: Temporal Entity-Relationship models a survey. IEEE Transactions on Knowledge and Data Engineering 11, 464–497 (1999)

    Article  Google Scholar 

  10. Halpin, T.: Information Modeling and Relational Databases: From conceptual analysis to logical design using ORM with ER and UML. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  11. Hay, D.C.: Data Model Patterns: Conventions of Thought. Dorset House Publishing (1996)

    Google Scholar 

  12. Inmon, W.H.: Building the Data Warehouse, 3rd edn. John Wiley & Sons, Chichester (2002)

    Google Scholar 

  13. Jensen, C.S., Snodgrass, R.T.: Temporal Data Management. IEEE Transactions on Knowledge and Data Engineering 11, 36–44 (1999)

    Article  Google Scholar 

  14. Khodorovskii, V.V.: On Normalization of Relations in Relational Databases. Programming and Computer Software 28(1), 41–52 (2002)

    Article  MathSciNet  Google Scholar 

  15. Kimball, R., Ross, M.: The Data Warehouse Toolkit: The complete guide to Dimensional Modeling, 2nd edn. Wiley Computer Publishing, Chichester (2002)

    Google Scholar 

  16. Li, X.: Building an Agile Data Warehouse: A Proactive Approach to Managing Changes. In: Proc. of the 4th IASTED Intl. Conf. (2006)

    Google Scholar 

  17. Nicola, M., Rizvi, H.: Storage Layout and I/O Performance in Data Warehouses. In: Proc. of the 5th Intl. Workshop on Design and Management of Data Warehouses (DMDW 2003), pp. 7.1–7.9 (2003)

    Google Scholar 

  18. Paulley, G.N.: Exploiting Functional Dependence in Query Optimization, PhD thesis, Dept. of Computer Science, University of Waterloo, Waterloo, Ontario, Canada (September 2000)

    Google Scholar 

  19. Regardt, O., Rönnbäck, L., Bergholtz, M., Johannesson, P., Wohed, P.: Analysis of normal forms for anchor models, http://www.anchormodeling.com/tiedostot/6nf.pdf valid at May 13th (2009)

  20. Rizzi, S., Golfarelli, M.: What time is it in the data warehouse? In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 134–144. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  21. Roddick, J.F.: A Survey of Schema Versioning Issues for Database Systems. Information and Software Technology 37(7), 383–393 (1995)

    Article  Google Scholar 

  22. Stonebraker, et al.: C-Store: A column-oriented DBMS. In: Proc. of the 31st VLDB Conference, VLDB Endowment, pp. 553–564 (2005)

    Google Scholar 

  23. Theodoratos, D., Sellis, T.K.: Dynamic data warehouse design. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, p. 802. Springer, Heidelberg (1999)

    Google Scholar 

  24. Watson, H.J., Ariyachandra, T.: Data Warehouse Architectures: Factors in the Selection Decision and the Success of the Architectures, Technical Report, Terry College of Business, University of Georgia, Athens, GA (July 2005)

    Google Scholar 

  25. Wikipedia, http://en.wikipedia.org/wiki/Temporal_database valid at April 11th (2009)

  26. Zimanyi, E.: Temporal Aggregates and Temporal Universal Quantification in Standard SQL. ACM SIGMOD Record 35(2), 16–21 (2006)

    Article  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Regardt, O., Rönnbäck, L., Bergholtz, M., Johannesson, P., Wohed, P. (2009). Anchor Modeling. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds) Conceptual Modeling - ER 2009. ER 2009. Lecture Notes in Computer Science, vol 5829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04840-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-04840-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04839-5

  • Online ISBN: 978-3-642-04840-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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