![](bg9.png)
nice amenity of making these model “composable”
in order to achieve the definition of models for Data
Mining problems arising in complex knowledge
discovery environments, and complex Data Mining
models starting from simpler ones play the major
roles.
Future work of this research is oriented towards
integrating within the framework (Cuzzocrea et al.,
2011) innovative aspects as to capture advanced
features of Data-Warehouse/Data-Mining platforms,
such as security and privacy, and uncertainty and
imprecision.
REFERENCES
Cai, Y. D., Clutterx, D., Papex, G., Han, J., Welgex, M.,
and Auvilx, L. (2004) ‘MAIDS: mining alarming
incidents from data streams’, in Proceedings of the
2004 ACM International Conference on Management
of Data, pages 919–920.
Cuzzocrea, A. (2007) ‘An OLAM-based framework for
complex knowledge pattern discovery in distributed-
and-heterogeneous-data-sources and cooperative
information systems’, in Proceedings of the 9th
International Conference on Data Warehousing and
Knowledge Discovery, LNCS Vol. 4654, pages 181–
198.
Cuzzocrea, A. (2009) ‘OLAP intelligence: meaningfully
coupling OLAP and data mining tools and
algorithms’, International Journal of Business
Intelligence and Data Mining, 4(3-4): 213–218.
Cuzzocrea, A. Furfaro, F., Masciari, E., and Saccà D.
(2009) ‘Improving OLAP analysis of
multidimensional data streams via efficient
compression techniques’, in A. Cuzzocrea (ed.),
“Intelligent Techniques for Warehousing and Mining
Sensor Network Data”, IGI Global, 17–49.
Cuzzocrea, A., Mazon, J.-N., Trujillo J., and Zubcoff, J.
(2011) ‘Model-driven data mining engineering: from
solution-driven implementations to “composable”
conceptual data mining models”, International
Journal of Data Mining, Modelling and Management,
to appear.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977)
‘Maximum likelihood from incomplete data via the
EM algorithm’, Journal of the Royal Statistical
Society, Series B, 39(1):1–38.
Frawley, W., Piatetsky-Shapiro, G., and Matheus, C.
(1992) ‘Knowledge discovery in databases: an
overview’, AI Magazine, 13(3): 213–228.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and
Widener, T. (1996) ‘The KDD process for extracting
useful knowledge from volumes of data’,
Communications of the ACM, 39(11):27–34.
Gaber, M. M., Zaslavsky, A. B., and Krishnaswamy, S.
(2005) ‘Mining data streams: a review’, SIGMOD
Record, 34(2): 18–26.
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A.,
Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh,
H. (1997) ‘Data cube: a relational aggregation
operator generalizing group-by, cross-tab, and sub-
totals’, Data Mining and Knowledge Discovery,
1(1):29–53.
Han, J. (1997) ‘OLAP mining: an integration of OLAP
with data mining’, in Proceedings of the 7th IFIP 2.6
Working Conference on Database Semantics, pages 1–
9.
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B. W., Wang, J.,
and Cai, Y. D. (2005) ‘Stream cube: an architecture
for multi-dimensional analysis of data streams’,
Distributed and Parallel Databases, 18(2): 173–197.
Heineman, G. T., and Councill, W. T. (2001) Component-
Based Software Engineering: Putting the Pieces
Together, Addison-Wesley Professional, Reading,
MA, USA.
Inmon, W. H. (1996) ‘The data warehouse and data
mining’, Communications of the ACM, 49(4)83–88.
Microsoft Research (2009a) Data Mining eXtensions
(DMX) Reference, http://msdn.microsoft.com/en-us/
library/ ms132058.aspx
Microsoft Research (2009b) SQL Server Analysis Services
– Data Mining, http://msdn.microsoft.com/en-us/
library/ bb510517.aspxQuinlan, J.R. (1986) ‘Induction
of decision trees’, Machine Learning, 1(1):81–106.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005a)
Introduction to data mining – Chapter 8: Cluster
analysis: basic concepts and algorithms, Addison-
Wesley, Reading, MA, USA.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005b)
Introduction to data mining – Chapter 9: Cluster
analysis: additional issues and algorithms, Addison-
Wesley, Reading, MA, USA.
Zubcoff, J., and Trujillo, J. (2006) ‘Conceptual modeling
for classification mining in data warehouses’, in
Proceedings of the 8th International Conference on
Data Warehousing and Knowledge Discovery, LNCS
Vol. 4081, pages 566–575.
Zubcoff, J., and Trujillo, J. (2007) ‘A UML 2.0 profile to
design association rule mining models in the
multidimensional conceptual modeling of data
warehouses’, Data & Knowledge Engineering,
63(1):44–62.
Zubcoff, J., Pardillo, J., and Trujillo, J. (2007a)
‘Integrating clustering data mining into the
multidimensional modeling of data warehouses with
UML profiles’, in Proceedings of the 9th International
Conference on Data Warehousing and Knowledge
Discovery, LNCS Vol. 4654, pages 199–208.
Zubcoff, J., Trujillo, J., and Cuzzocrea, A. (2007b) ‘On
the suitability of time series analysis on data
warehouses’, in Proceedings of the 1st IADIS
European Conference on Data Mining, pages 17–24.
A UML-EXTENDED APPROACH FOR MINING OLAP DATA CUBES IN COMPLEX KNOWLEDGE DISCOVERY
ENVIRONMENTS
289