1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2021/2022
4. Computer Science - Second-cycle Erasmus programme
5. Data warehouses

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Data warehouses - course description

Aim of the course

• acquaint students with architectures of data warehouses and multidimensional data models,
• acquaint students with the basic methods of data mining,
• shaping basic skills in the practical construction of the data warehouse.

Prerequisites

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Databases, Elements of artificial intelligence.

Scope

Introduction. Decision support systems. Operational processing versus analytical processing.

Data warehouses. Definition of Data Warehouse. Features of Data Warehouse. Exemplary applications. Architectures of Data Warehouses. Layered structure of the Warehouse: data sources, extraction layer, cleaning, transformation and data loading, data access layer. Tools for designing, building, maintaining and administering of the Data Warehouse.

Multidimensional data models. Models: MOLAP, ROLAP, HOLAP. Building of exemplary data cube.

Knowledge representation forms: logical rules, decision trees, neural nets.

Data Mining. Data preparation process. Selected Data Mining methods: classification, grouping, discovering association and sequences, analysis of time series. 

Exemplary Data Mining applications.

Teaching methods

• Lecture: conventional/traditional lecture with elements of discussion.
• laboratory: work in the groups, practical excersises.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture – obtaining a positive grade from exam.

Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.

 

Calculation of the final grade: lecture 50% + laboratory 50%

Recommended reading

1. Hand D., Mannila H., Smyth P.: Principles of Data Mining. Massachusetts Institute of Technology, 2001.
2. Jarke M., Lenzerini M., Vassiliou Y., Vassiliadis P.: Fundamentals of Data Warehouses. Springer-Verlag, Berlin, 2002.
3. Larose D.T.: Discovering Knowledge in Data. An Introduction to Data Mining.  John Wiley & Sonc, Inc., 2005.
4. Larose D.T.: Data Mining Methods and Models. John Wiley & Sonc, Inc., 2006.
5. Rutkowski L.: Computational Intelligence. Methods and Techniques. Springer-Verlag, Berlin, 2008.

Further reading

1. Poe V., Klauer P., Brobst S.: Building a Data Warehouse for Decision Support. Prentice-Hall, Inc., a Simon & Schuster Company, 1999.
2.  Miczulski W., Szulim R.:Using time series approximation methods in the modelling of industrial objects and processes. Measurements models systems and design / ed. by J. Korbicz .- Warszawa : Wydawnictwo Komunikacji i Łączności, 2007 - s. 157--174.
3.  Miczulski W., Sobolewski Ł.: Algorithm for Predicting [UTC–UTC(k)] by Means of Neural Networks, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,   8/2017, s. 2136 - 2142. 

Notes

Modified by dr hab. inż. Wiesław Miczulski, prof. UZ (last modification: 14-07-2021 14:43)