[1]Fayyad M.U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R.:Advances in
Knowledge Discovery and Data Mining, USA: AAAI Press, 1996
[2] Inmon W.H., Building the Data Warehouse, Third Edition, USA, Wiley
Computer Publishing, 2002
[3]Cook book Data Mining Using Enterprise Miner software: A Case Study
Approach published by company SAS Institute , February 2000
Terminated in academic year 2019/2020
Knowledge Discovery in Databases
| Type of study | Follow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 545-0011/01 |
| Abbreviation | DBZN |
| Course title | Knowledge Discovery in Databases |
| Credits | 5 |
| Coordinating department | Department of Economics and Control Systems |
| Course coordinator | Ing. Jiří Švub, Ph.D. |
Subject syllabus
1.Knowledge Management - essential features in the application of company analysis
terms data, information, knowledge, organization division based on the type of knowledge,
they work. Stages of knowledge management.
2.Business Intelligence (BI) - explanation of the concept of integrating business
processes, data integration, enterprise integration with the surroundings. BI Architecture
Chosen by example.
3.Knowledge Discovery in Databases - explanations of concepts, characteristics
various stages.
4.Podstata data mining, important features, a description of the methods used.
Job Structure for Data Mining an example methodology SEMM.
5.Datové dice - store data in OLAP systems, database schemas,
techniques used for data analysis.
6.Profesionální systems for knowledge discovery in databases, an example
any practical application.
7.Stručná characteristics of mining unstructured data -
representation of the document as a vector.
8.Možnosti systems for searching unstructured data due
ability to understand the content of the text.
9.Used UB-tree search in a multidimensional vector
space.
10.Podstata warehouse, a dual approach to architecture, analysis
Fundamental differences between operating systems and data warehouses. Tools
used to build and operate a data warehouse.
11.Design data warehouse database schema - tables of facts and dimensions,
time factor, granularity of data.
12.Funkce metadata in data warehousing, administrative metadata, user
metadata.
terms data, information, knowledge, organization division based on the type of knowledge,
they work. Stages of knowledge management.
2.Business Intelligence (BI) - explanation of the concept of integrating business
processes, data integration, enterprise integration with the surroundings. BI Architecture
Chosen by example.
3.Knowledge Discovery in Databases - explanations of concepts, characteristics
various stages.
4.Podstata data mining, important features, a description of the methods used.
Job Structure for Data Mining an example methodology SEMM.
5.Datové dice - store data in OLAP systems, database schemas,
techniques used for data analysis.
6.Profesionální systems for knowledge discovery in databases, an example
any practical application.
7.Stručná characteristics of mining unstructured data -
representation of the document as a vector.
8.Možnosti systems for searching unstructured data due
ability to understand the content of the text.
9.Used UB-tree search in a multidimensional vector
space.
10.Podstata warehouse, a dual approach to architecture, analysis
Fundamental differences between operating systems and data warehouses. Tools
used to build and operate a data warehouse.
11.Design data warehouse database schema - tables of facts and dimensions,
time factor, granularity of data.
12.Funkce metadata in data warehousing, administrative metadata, user
metadata.
Literature
Advised literature
1. Cook book Getting Started with Enterprise Miner Software, Version 4.0
published by company SAS Institute , July 2000
published by company SAS Institute , July 2000