Data management technologies

Course topics:
External data management:
Databases and data warehouses
Database design:
conceptual, logical and physical design
advanced normalization,
performance optimization
distributed databases
Data warehouse design:
design methodologies,
data quality assurance,
data analysis
Non-relational database design (NoSQL)
Non-relational data modelling

Internal data management:
Assuring availability and consistency of stored data:
concurrent data access,
data archival and recovery
distributed and parallel databases
Query evaluation and optimization:
query execution planning,
estimating the costs of basic operations,
alternative plan considerations
Management of semi-structured and unstructured data types:
Modern non-relational database systems
spatial and temporal data,
other semi-structured data (audio, video, images, sequences, JSON, XML)

Tutorial topics:
Recognize typical data management problems and approaches for solving them
Get to know various tools for database design and utilization, and use them in practical problems.
Using the products of aforementioned tools for a practical database implementation (in terms of a substantial project)
Through the tutorial students get familiar with various data management tools and use them - in course of their projects – as a part of a practical problem solution. The final part of the project is a public presentation of the assigned problem, its solution and results.

1. T. M. Connolly, C. E. Begg: Database Systems: A Practical Approach to Design, Implementation and Management, 4th edition, Addison Wesley, 2004.
2. S. Sumathi, S. Esakkirajan: Fundamentals of Relational Database Management Systems, Springer, 2007.
3. R. Ramakrishnan, J. Gehrke: Database Management Systems, 3rd edition, McGraw-Hill, 2002.
4. Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement, Pragmatic Bookshelf, 2012

The main course objective is to present principles and approaches to data management from two points of view: external, focusing on proper database/data warehouse design and data preparation, and internal, focusing on intrinsic key database technologies.

General competences:
ability of critical thinking
developing skills in critical, analytical and synthetic thinking
the ability to define, understand and solve creative professional challenges in computer and information science,
compliance with security, functional, economic and environmental principles
the ability to apply acquired knowledge in independent work for solving technical and scientific problems in computer and information science, the ability to upgrade acquired knowledge

Subject specific competences:
The ability to understand and apply computer and information science knowledge to other technical and relevant fields (economics, organisational science, etc)
practical knowledge and skills of computer hardware, software and information technology necessary for successful professional work in computer and information science
the ability to independently perform both less demanding and complex engineering and organisational tasks in certain narrow areas and independently solve specific well-defined tasks in computer and information science

Knowledge and understanding:
Recognizing data management problems, and understanding principles and approaches for solving them. Comprehension of basic concepts and usability of non-relational (NoSQL) databases.
Application:
Using acquired knowledge and tools for data management in engineering and research work.

Reflection:
Introduction and comprehension of connections between specific theoretical data management technologies, and their practical use.
Transferable skills:
Database design, data storage, management and analysis are directly or indirectly being used in information systems, business intelligence, web services and intelligent systems.

Lectures, homework and project work with explicit focus on simultaneous studies (for homeworks) and teamwork (for projects).

Continuing (homework, midterm exams, project work)
Final (written or oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

KONONENKO, Igor, KUKAR, Matjaž. Machine learning and data mining : introduction to principles and algorithms. Chichester: Horwood Publishing, cop. 2007. XIX, 454 str., ilustr. ISBN 1-904275-21-4. ISBN 978-1-904275-21-3. [COBISS-SI-ID 5961556]
PETELIN, Boris, KONONENKO, Igor, MALAČIČ, Vlado, KUKAR, Matjaž. Multi-level association rules and directed graphs for spatial data analysis. Expert systems with applications, ISSN 0957-4174. [Print ed.], 2013, vol. 40, issue 12, str. 4957-4970. , doi: . [COBISS-SI-ID 2761807]
KUKAR, Matjaž, KONONENKO, Igor, GROŠELJ, Ciril. Modern parameterization and explanation techniques in diagnostic decision support system : a case study in diagnostics of coronary artery disease. Artificial intelligence in medicine, ISSN 0933-3657. [Print ed.], Jun. 2011, vol. 52, no. 2, str. 77-90, ilustr. [COBISS-SI-ID 8991060]
ŠAJN, Luka, KUKAR, Matjaž. Image processing and machine learning for fully automated probabilistic evaluation of medical images. Computer methods and programs in biomedicine, ISSN 0169-2607. [Print ed.], Dec. 2011, vol. 104, no. 3, str. 75-86, ilustr. [COBISS-SI-ID 8333652]
KUKAR, Matjaž. Quality assessment of individual classifications in machine learning and data mining. Knowledge and information systems, ISSN 0219-1377. [Print ed.], 2006, vol. 9, no. 3, str. [364]-384, graf. prikazi. [COBISS-SI-ID 5282900]