Improving Expressive Power in Modeling Data Warehouse and OLAP Applications - Evolving Application Domains of Data Warehousing and Mining - page 38

scenario for the analysis of sales and represented
different kinds of hierarchies and dimensions
at the conceptual, logical and implementation
levels, for which we use the MultiDim model,
relational tables, and MicrosoftAnalysis Services,
respectively. By means of examples, we have
demonstrated how the use of the conceptual model
can allow a better understanding of the different
elements that are required by users for analysis
purposes. Then, even though mapping into the logi-
cal schema leads to well-known structures, e.g.,
to snowflake schema, the application semantic is
better understood by using the conceptual model.
Consequently, this conceptual representation may
help designers choose adequate options avail-
able for implementing OLAP cubes, such as the
ones included in Microsoft Analysis Services for
unbalanced or non-covering hierarchies, and for
role-playing or degenerate dimensions.

Golfarelli, M., & Rizzi, S. (1998).A methodologi-
cal framework for data warehouse design. Pro-
ceedings of the 1 st ACM International Workshop
on Data Warehousing and OLAP, (pp. 3-9).

Gray, J., Chaudhuri, S., Basworth, A., Layman,
A., Reichart, D., & Venkatrao, M. (1997). Data
cube: a relational aggregation operator general-
izing group-by, cross-tab, and sub-totals. Data
Mining and Knowledge Discovery , 1 (1), 29-53.
doi:10.1023/A:1009726021843

Hahn, K., Sapia, C., & Blaschka, M. (2001).
Automatically generating OLAP schemata from
conceptual graphical models. Proceedings of the
4 th ACM International Workshop on Data Ware-
housing and OLAP , (pp. 9-16).

Hümmer, W., Lehner, W., Bauer, A., & Schle-
singer, L. (2002).A decathlon in multidimensional
modeling: open issues and some solutions. Pro-
ceedings of the 4 th International Conference on
Data Warehousing and Knowledge Discovery ,
(pp. 275-285).

referenceS

Abelló, A., Samos, J., & Saltor, F. (2006). YAM 2 :
a multidimensional conceptual model extending
UML. Information Systems , 32 (6), 541-567.
doi:10.1016/j.is.2004.12.002

Hurtado, C., & Gutierrez, C. (2007). Handling
structural heterogeneity in OLAP In R. Wrembel &
Ch. Koncilia (Eds.). Data warehouses and OLAP:
concepts, architectures and solutions , (pp. 27-57).
Hershey, PA: Idea Group Publishing.

Akoka, J., Comyn-Wattiau, I., & Prat, N. (2001).
Dimension hierarchies design from UML gener-
alizations and aggregations. Proceedings of the
20 th International Conference on Conceptual
Modeling , (pp. 442-445).

Hüsemann, B., Lechtenbörger, J., & Vossen, G.
(2000). Conceptual data warehouse design. Pro-
ceedings of the 2 nd International Workshop on
Design and Management of Data Warehouses ,
(p. 6).

Bauer, A., Hümmer, W., & Lehner, W. (2000). An
alternative relational OLAP modeling approach.
Proceedings of the 2 nd International Conference
on Data Warehousing and Knowledge Discovery ,
(pp. 189-198).

Jagadish, H., Lakshmanan, L., & Srivastava, D.
(1999). What can hierarchies do for data ware-
houses. Proceedings of the 25th International
Conference on Very Large Data Bases , (pp. 530-
541).

Elmasri, R., & Navathe, S. (2006). Fundaments
of database systems (5 th Ed.). Reading, MA: Ad-
dison Wesley.

Kimball, R., & Ross, M. (2002). The data ware-
house toolkit: the complete guide to dimensional
modeling. Hoboken, NJ: Wiley.