Abstract
From the point of view of a data warehouse system its part of collecting and receiving information from other systems is crucial for all subsequent business intelligence applications. The incoming information can be classified generally in two types, the state-snapshot data and the state-change or event data usually called transactional data, which contains information about the change processes applied on the instances of information objects. On the way towards active data warehouses it becomes more important to provide complete data with minimal latency. We focus in this paper on dimensional data provided by any data-master application. The information transfer is done via messages containing the change-information of the dimension instances. The receiving data warehouse system is able to validate the event-messages, reconstruct the complete history of the dimension and provide a well applicable “comprehensive slowly changing dimension” (cSCD) interface for well-performing queries on the historical and current state of the dimension. A prototype implementation of “active integration” of a data warehouse is proposed.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sen, A., Sinha, A.P.: A Comparison of Data Warehousing Methodologies. Communications of the ACM 48(3) (March 2005)
Bliujute, R., Saltenis, S., Slivinskas, G., Jensen, C.S.: Systematic Change Management in Dimensional Data Warehousing. In: Proc. of the 3rd Intl. Baltic Workshop on Databases and Information Systems, Riga, Latvia, pp. 27–41 (1998)
Bruckner, R., Tjoa, A.: Managing Time Consistency for Active Data Warehouse Environments. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 254–263. Springer, Heidelberg (2001)
Brobst, S.: Enterprise Application Integration and Active Data Warehousing. In: Proc. Data Warehousing 2002, pp. 15–22. Physica, Heidelberg (2002)
Hohpe, G., Woolf, B.: Enterprise Integration Patterns, Designing, Building, and Deploying Messaging Solutions. Addison-Wesley, Reading (2004)
Inmon, W.: Building the Data Warehouse, 2nd edn. Jon Wiley & Sons, Chichester (1996)
Kimball, R., et al.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, Chichester (2002)
Koncilia, C., Eder, J.: Changes of Dimension Data in Temporal Data Warehouses. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 284–293. Springer, Heidelberg (2001)
Meta Object Facility (MOF) Specification, http://www.omg.org/docs/formal/00-04-03.pdf
Rieger, B., Brodmann, K.: Mastering Time Variances of Dimension Tables in the Data Warehouse, Osnabrueck University (1999)
Rocha, R., Cardoso, F., Souza, M.: Performance Tests in Data Warehousing ETLM Process for Detection of Changes in Data Origin. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 129–139. Springer, Heidelberg (2003)
TIBCO Software Inc., http://www.tibco.com
Vandermay, J.: Considerations for Building a Real-time Oracle Data Warehouse. DataMirror Corporation White Paper (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, T.M., Nemec, J., Windisch, M. (2005). Event-Feeded Dimension Solution. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_3
Download citation
DOI: https://doi.org/10.1007/11546849_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
eBook Packages: Computer ScienceComputer Science (R0)