â¢GUIDA ECONOMIA 07-08 - Università degli studi di Udine
â¢GUIDA ECONOMIA 07-08 - Università degli studi di Udine
â¢GUIDA ECONOMIA 07-08 - Università degli studi di Udine
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prospectus u<strong>di</strong>ne<br />
183<br />
2. Model selection methods.<br />
3. Non parametric estimation: local<br />
regression and spline.<br />
4. Classification methods.<br />
Textbook<br />
- A. AZZALINI, B. SCARPA, Analisi dei dati e<br />
data mining, Springer Verlag Italia, 2004.<br />
DATA WAREHOUSING<br />
Prof.ssa Anna Marzona<br />
Aims<br />
The course supplies the fundamental<br />
knowledge on information systems, covering<br />
Data Warehousing, multi<strong>di</strong>mensional<br />
databases, OLAP Analysis. The<br />
course follows the steps required to build<br />
data warehousing systems, looking in<br />
depth at the conceptual and logical design<br />
of multi<strong>di</strong>mensional databases. The final<br />
lessons introduce data mining as an<br />
extension of data warehousing systems.<br />
Real cases taken from business environments<br />
are given as examples and used to<br />
experience the design process.<br />
Contents<br />
- Decision Support Systems (DSS). Motivations,<br />
methodologies and architecture.<br />
- Differences between information and<br />
operational systems. Information Systems:<br />
the multi<strong>di</strong>mensional model and<br />
OLAP analysis. Data Warehouse and<br />
Data Mart: definitions, usage objectives,<br />
<strong>di</strong>fferences.<br />
- Data Warehouse Design: data warehousing<br />
system lifecycle. Needs and data<br />
source analyses. Data source reconciliation.<br />
Main architectural solutions. A multi<strong>di</strong>mensional<br />
conceptual model: Data<br />
Fact Model (DFM). Logical models:<br />
MOLAP, ROLAP, HOLAP. Star and<br />
Snowflake schemas. The treatment of<br />
variations in both facts and <strong>di</strong>mensions.<br />
- Update policies. Generation and updating<br />
of the multi<strong>di</strong>mensional data structure.<br />
Total and incremental fee<strong>di</strong>ng<br />
processes. Extraction, Transformation<br />
and data Loa<strong>di</strong>ng (ETL): main problems<br />
to be solved, examples of applicable solutions.<br />
- Tools for Data analysis: Reporting,<br />
OLAP browsers, Data Mining. Explanation<br />
of the Hypotheses driven approach<br />
and OLAP analysis.<br />
- Introduction to Data Mining: fundamental<br />
problems, application environments,<br />
practical aspects.<br />
- Analysis and design of a real-world casestudy:<br />
from the entity-relation model to<br />
the multi<strong>di</strong>mensional conceptual and<br />
logical models.<br />
Pre-requisites<br />
The students should have a good knowledge<br />
of the program covered in the course<br />
‘Basi <strong>di</strong> dati (Database)’<br />
Bibliography<br />
Recommended texts<br />
- M. GOLFARELLI, S. RIZZI, Data Warehouse:<br />
teoria e pratica della progettazione,<br />
McGraw-Hill, 2006 (in Italian).<br />
- M. PIGHIN, A. MARZONA, Sistemi Informativi<br />
Aziendali - Struttura e applicazioni,<br />
Pearson Italia, 2004 (Parte 3: I Sistemi<br />
Informazionali) (in Italian).<br />
- Ad<strong>di</strong>tional materials available on the<br />
course’s Web Page<br />
Further rea<strong>di</strong>ng<br />
- M. PIGHIN, A. MARZONA, Sistemi Informativi<br />
Aziendali - Struttura e applicazioni,<br />
Pearson Italia, 2004 (Part 1 and Part 2)<br />
(in Italian).<br />
- G. BRACCHI, G. MOTTA, Processi Aziendali<br />
e Sistemi Informativi, Franco Angeli,<br />
2000 (in Italian).<br />
- ATZENI, CERI, PARABOSCHI, TORLONE,<br />
Database Systems: Concepts, Languages &<br />
Architectures, McGraw-Hill, 1999 (in English).<br />
- R. KIMBALL, M. ROSS, The Data Ware-
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