1. SylabUZ
2. Wydział Nauk Inżynieryjno-Technicznych
3. semestr zimowy 2023/2024
4. Informatyka - Program Erasmus drugiego stopnia
5. Data warehouses

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Data warehouses - 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, Elements of artificial intelligence.

Zakres tematyczny

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.

Metody kształcenia

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

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 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%

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.  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. 
4. Sobolewski, Ł.; Miczulski, W. Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network. Appl. Sci. 2021, 11, 5615. https://doi.org/10.3390/app11125615.

Uwagi

Zmodyfikowane przez dr hab. inż. Wiesław Miczulski, prof. UZ (ostatnia modyfikacja: 21-04-2023 21:15)