A conceptual solution for representing time in data warehouse dimensions

Data Warehouses (DWs) use an omnipresent time dimension for keeping track of changes in measure values. However, this dimension cannot be used to model changes in other dimensions. On the other hand, Temporal Databases (TDBs) have been successfully used for modelling time-varying information. Bringing together these two research areas, leading to Temporal Data Warehouses (TDWs), provides the necessary solutions for managing time-varying data in dimensions. In this paper, we introduce temporal extensions for the MultiDimER model, a conceptual multidimensional model. In our model we allow the inclusion of valid and transaction time, which are obtained from source systems, in addition to the data warehouse loading time. Our model allows a conceptual representation of time-varying levels, attributes, and hierarchies. For the latter, we discuss different cases depending on whether the changes in levels affect the relationships between them.

[1]  Alberto Abelló,et al.  A Bitemporal Storage Structure for a Corporate Data Warehouse , 2003, ICEIS.

[2]  Stefano Spaccapietra,et al.  TERC+ : a temporal conceptual model , 1997 .

[3]  Stephen R. Gardner.  Building the data warehouse , 1998, CACM.

[4]  Alberto O. Mendelzon,et al.  Time in Multidimensional Databases , 2003, Multidimensional Databases.

[5]  Jennifer Widom,et al.  Maintaining Temporal Views over Non-Temporal Information Sources for Data Warehousing , 1998, EDBT.

[6]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[7]  Esteban Zimányi,et al.  Object-Relational Representation of a Conceptual Model for Temporal Data Warehouses , 2006, CAiSE.

[8]  Olivier Teste,et al.  A Temporal Object-Oriented Data Warehouse Model , 2000, DEXA.

[9]  Johann Eder,et al.  The COMET Metamodel for Temporal Data Warehouses , 2002, CAiSE.

[10]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling , 1996 .

[11]  Alberto O. Mendelzon,et al.  Temporal Queries in OLAP , 2000, VLDB.

[12]  Christian S. Jensen,et al.  Systematic Change Management in Dimensional Data Warehousing , 1998 .

[13]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[14]  Ramez Elmasri,et al.  A temporal model and query language for ER databases , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[15]  Matthias Jarke,et al.  Fundamentals of Data Warehouses , 2000, Springer Berlin Heidelberg.

[16]  Maurizio Rafanelli.  Multidimensional Databases: Problems and Solutions , 2003 .

[17]  Esteban Zimányi,et al.  OLAP Hierarchies: A Conceptual Perspective , 2004, CAiSE.

[18]  A Min Tjoa,et al.  Capturing Delays and Valid Times in Data Warehouses—Towards Timely Consistent Analyses , 2002, Journal of Intelligent Information Systems.

[19]  Alberto Abelló,et al.  A Temporal Study of Data Sources to Load a Corporate Data Warehouse , 2003, DaWaK.

[20]  Alberto O. Mendelzon,et al.  Maintaining data cubes under dimension updates , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[21]  Christian Koncilia.  A Bi-Temporal Data Warehouse Model , 2003, CAiSE Short Paper Proceedings.

[22]  Ramez Elmasri,et al.  Fundamentals of Database Systems, 5th Edition , 2006 .

[23]  Esteban Zimányi,et al.  Hierarchies in a multidimensional model: From conceptual modeling to logical representation , 2006, Data Knowl. Eng..

[24]  Richard T. Snodgrass,et al.  The TSQL2 Temporal Query Language , 1995 .

[25]  Christian S. Jensen,et al.  A foundation for capturing and querying complex multidimensional data , 2001, Inf. Syst..

[26]  Christian S. Jensen,et al.  Conceptual Modeling of Time-Varying Information , 2004, CCCT.