48113 - Decision-Making Support Information Systems

Learning outcomes

At the end of the course the student will know the design of information system and applications on database management systems (DBMS). In particular, the student will

• know the role and main features and functionalities of information systems in an organization
• know a methodology for the design of databases, and how to apply it to user requirements in natural language
• know a graphical software tool for design support
  
• be able to read and interpret design schemata
• be able to use a relational DBMS
  

Course contents

Relational databases

• The relational model of data
• Attributes
• Relation and database schema
• Tuple, relation and database
• Relational algebra
• Integrity constraints
• Keys and superkeys
• Referential integrity
• DBMS architecture (outline)

  The SQL language

• Database creation, querying and updating
• Transaction and authentication management

Relational database design

• Dependency theory
• Normal forms
• Armstrong's axioms

Entity-Relationship conceptual design

• Entity, relationship, attribute
• Generalization hierarchy
• Identifier
• ER schema design from natural language requirements

Logical design by mapping an ER schema to a relational schema

• Selection of primary keys, elimination of generalization hierarchies, elimination of external identifiers
• Elimination of composite and multivalued attributes
• Translation of entities and relationships into relation schemata

Data warehousing and OLAP

• OLTP and OLAP
• The multidimensional model of data: Facts, measures, dimensions, hierarchies, cuboids
• Star schema, snowflake schema, galaxy schema
• Operations in the multidimensional model: roll-up, drill-down, slice and dice, pivot, data cube
  
• Data warehouse: Definition, design, architecture

Knowledge Discovery in Databases

• Association rule discovery
• Classification of association rules
• Apriori algorithm
• FP-growth algorithm (outline)
  
• Data clustering
• One-pass algorithm
• BIRCH algorithm
  
• k-means algorithm
• EM algorithm
• Supervised classification
• Classification trees
• Support vector models

Laboratory classes

• Relational DBMS IBM DB2, Microsoft SQL Server, Microsoft Access, SQL language.
• Building a cube in Microsoft SQL Server
• Data mining tasks in Microsoft SQL Server and IBM DB2 Warehouse Edition

Readings/Bibliography

• Atzeni, P., Ceri, S., Paraboschi, S., & Torlone, R. (2009). Basi di dati. Modelli e linguaggi di interrogazione. Milano: McGraw-Hill.
• Van der Lans, R. F. (2001). Introduzione a SQL (2a ed.). Milano: Addison-Wesley.
• Han, J., & Kamber, M. (2006). Data Mining. Concepts and Techniques. San Francisco, CA: Morgan Kaufmann.

Teaching methods

• The theoretical and practical notions for the design of queries, for the management of a database, for the design of database schemata, and for knowledge discovery in databases are explained in frontal classes.
• In laboratory classes, students are encouraged to design increasingly difficult SQL queries and to test their correctness on the DBMS at hand,  and to generate data mining models using the tools integrated into the DBMSs.
  

Assessment methods

• Preliminary examination on database design in the ER language and database queries in SQL
  
• Oral examination

Teaching tools

• PC and overhead projector
• Laboratory with desktop PCs, relational DBMS IBM DB2, Microsoft SQL Server, Microsoft Access

Links to further information

http://www-db.deis.unibo.it/~slodi/SISD/2011-2012/sisd.html

Office hours

See the website of Stefano Lodi