- Department of Software Engineering
The aim of the subject is to explain to the students different characteristics of production and analytical databases and a set of processes, know-how and tools (not only) to support decision-making activities within the organization. In addition to the basic concept of BI, listeners will get acquainted with the general methodology of implementation of custom algorithms derived from other theories and subjects into the BI environment.
The subject extends and further develops knowledge of 18DATS, 18SQL and 01REDA subjects and assumes knowledge of statistical methods.
- Syllabus of lectures:
Structure of the lectures:
1. The principles of Business Intelligence (BI)
2. Production database vs. analytic database
3. The process of extraction, transformation and load (ETL)
4. Data warehouse: architecture, facts and dimensions, granularity
5. Data mart
7. Data cubes, OLAP
8. Data Mining
9. Implementation of sophisticated mathematical methods and algorithms in the BI environment
10. Specialized tools of BI
11. Knowledge Management
13. Data Quality Assurance
- Syllabus of tutorials:
1. Deployment of the Microsoft SQL Server (MSSQL), tools for administration and development
2. MSSQL: database modelling
3. MSSQL: integration services 1
4. MSSQL: integration services 2
5. MSSQL: reporting services
6. MSSQL: analysis services 1
7. MSSQL: analysis services 2
8. MSSQL: data mining 1
9. MSSQL: data mining 2
10. Case studies 1
11. Case studies 2
12. Case studies 3
13. Presentations of the students? works
- Study Objective:
Knowledge: Understanding of the difference between production and analytic database and of a set of processes, knowhow and tools to support analytic and decision-making activities within the organization. In addition to the basic concept of BI, students will get acquainted with the general methodology of implementing custom algorithms derived from other theories and subjects into the BI environment.
Abilities: Sufficient for overall deployment of a BI platform and for implementation of specific algorithms inside its components, as well.
- Study materials:
 KIMBALL, Ralph. The data warehouse lifecycle toolkit. 2nd ed. Indianapolis, IN: Wiley Pub., c2008, 636 s. ISBN
 VERCELLIS, Carlo. Business intelligence: data mining and optimization for decision making. Chichester, U.K.:
Wiley, 2009, 417 s. ISBN 04-705-1139-7.
 LANGIT, Lynn. Smart business intelligence solutions with Microsoft SQL Server 2008. Redmond, Wash.: Microsoft,
c2009, 765 s. ISBN 07-356-2580-8.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: