Laboratorio di business intelligence

Theoretical notions required by the module are taught in the courses of the first semester 426AA “Basi di dati di supporto alle decisioni”, 420AA “Data mining I”, and in the course of the second semester “Data Mining II”.

The module presents technologies and systems for data access, for building and analysing datawarehouses, for reporting, and for knowledge discovery from data. The focus is on tools, systems and problem solving methodologies, with case studies and applicative problems. The course also provides useful means for the typical work performed during the final thesis.

The student will have knowledge about the main software technologies for accessing data; for designing and developing datawarehouses, OLAP data cubes, and reports; and for extracting and applying predictive data mining models.

The student will be able to use software tools for the design of datawarehouses, for their population through ETL flows, for the design and the query of OLAP data cubes, for the design of reports and dashboards in support of business decisions.  The student will be also able to apply data mining tools to extract models from data, with special reference to predictive models for marketing and CRM.

The student will be able to assess, with indepence and autonomy, the current and future software technologies for Business Intelligence with regard to the requirements of a specific data analysis task.

Introduction
Introduction, objectives. The architecture of Business Intelligence.

Data access
File data access. Formats: CSV, FLV, ARFF, XML, binary and compressed. API Java. Standards for RDBMS data access: ODBC, JDBC, OLE DB, ADO. Details on API JDBC. Tools: Java, SQL Server 2012. Lab practice. 

The Extract Transform and Load (ETL) process 
Data access, selection, cleaning and transformation tasks. Populating a datawarehouse: surrogate keys and slowly changing dimensions. Tools: Java, SQL Server 2012 Integration Services. Lab practice. 

Data warehousing, OLAP and reporting
Querying a datawarehouse with analytic SQL. Building OLAP data cubes. Querying OLAP data cubes with MDX. Reporting with Excel pivot tables. Designing reports and dashboards. Tools: SQL Server 2012 Analysis Services, Microsoft Excel, SQL Server 2012 Reporting Services. Lab practice. 

Knowledge discovery
The knowledge discovery process. Data mining models: classification, association rules, clustering. Tools: Weka Explorer and Knowledge Flow. Lab practice. 

Articles, book chapters, and manuals will be provided at the course web site. Software tools will be downloadable with an academic licence.