1. SylabUZ
2. Wydział Nauk Inżynieryjno-Technicznych
3. semestr zimowy 2021/2022
4. Informatyka - Program Erasmus pierwszego stopnia
5. Data warehouse and data mining

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Data warehouse and data mining - opis przedmiotu

Cel przedmiotu

• 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.

Wymagania wstępne

Zapisz zmiany

Databases

Zakres tematyczny

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. 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.

Metody kształcenia

• lecture: conventional/traditional lecture with elements of discussion.
• laboratory: work in the groups, practical excersises.
• project:  the main condition to get a pass are sufficient marks for individual task conducted during the semester. There is also possible, that larger tasks can be carried out in groups, but each student will be assessed individually.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture – obtaining a positive grade from test.

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

Project -  the main condition to get a pass are sufficient marks for individual task conducted during the semester. There is also possible, that larger tasks can be carried out in groups, but each student will be assessed individually.

Calculation of the final grade: lecture 33,3% + laboratory 33,3% + 33,3% project

Literatura podstawowa

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.

Literatura uzupełniająca

1. Poe V., Klauer P., Brobst S.: Building a Data Warehouse for Decision Support. Prentice-Hall, Inc., a Simon & Schuster Company, 1999.
2. Sobolewski Ł., Miczulski W.: Methods od constructing time series for predicting local time scales by means of a GMDH-type Neural Network. Appl. Sci. 2021,11, 5615.

Uwagi

Zmodyfikowane przez prof. dr hab. inż. Andrzej Obuchowicz (ostatnia modyfikacja: 18-07-2021 16:14)