Data Storage and Preparation

• 26 hrs lectures
• 6 hrs exercises
• 6 hrs pc labs
• 14 hrs projects

• 60 pts final exam
• 20 pts mid-term test
• 20 pts projects

Students will be able to store and manipulate data in suitable database systems, to explore data and prepare data for modelling within knowledge discovery process.

• Student is better able to work with data in various situations.
• Student improves in solving small projects in a small team.

The aim of the course is to explain the historical development of database technologies, motivation of knowledge discovery from data and basic steps of knowledge discovery process, to explain essence, properties and the use of extended relational and NoSQL databases as data sources for knowledge discovery and to explain approaches and methods used for data understanding and data pre-processing for knowledge discovery.

The aim of this course is to demonstrate how to work with complex data around us, how to store such data, how to get oriented in such data, obtain useful descriptive characteristics from such data, and how to prepare such data for extraction of hidden information/knowledge by application of machine learning methods and other advanced analytical methods.

• Fundamentals of relational databases and SQL.
• Object-oriented paradigm.
• Fundamentals of XML.
• Fundaments of computational geometry.
• Fundaments of statistics and probability.

• Dunckley, L.: Multimedia Databases: An Object-Relational Approach. Pearson Education, 2003, p. 464, ISBN 0-201-78899-3

• Lemahieu, W., Broucke, S., Baesens, B.: Principles of Database Management. Cambridge University Press. 2018, 780 p.
• Kim, W. (ed.): Modern Database Systems, ACM Press, 1995, ISBN 0-201-59098-0
• Melton, J.: Advanced SQL: 1999 - Understanding Object-Relational and Other Advanced. Morgan Kaufmann, 2002, 562 s., ISBN 1-558-60677-7
• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, p. 703, ISBN 978-0-12-381479-1
• Skiena, S.S.: The Data Science Design Manual. Springer, 2017, 445 s. ISBN 978-3-319-55443-3.
• Shekhar, S., Chawla, S.: Spatial Databases: A Tour, Prentice Hall, 2002/2003, 262 s., ISBN 0-13-017480-7
• Gaede, V., Günther, O.: Multidimensional Access Methods, ACM Computing Surveys, Vol. 30, No. 2, 1998, s. 170-231.

1. History of database technology and knowledge discovery, process of knowledge discovery.
2. Object-oriented approach in databases.
3. NoSQL databases I - introduction to NoSQL, CAP theorem and BASE, key-value databases, data partitioning and distribution.
4. NoSQL databases II -data models in NoSQL databases (column, document, and graph databases), querying and data aggregation, NewSQL databases.
5. Web scraping.
6. Data preparation - data understanding: descriptive characteristics, visualization techniques, correlation analysis.
7. Data preparation - data pre-processing I: data cleaning and integration.
8. Data preparation - data pre-processing II: data reduction, imbalanced data, data transformation, other data pre-processing tasks.
9. Mid-term exam
10. Languages and systems for knowledge discovery, real case studies.
11. Support for working with XML and JSON documents in databases.
12. Spatial databases.
13. Indexing of multidimensional data.

DEMO excercises

1. Object-relational and spatial databases, data definition and manipulation, peculiarities
2. Multimedia and XML databases, data indices
3. NoSQL databases

1. Application binding to object-relational databases, application building in spatial databases
2. Multimedia and XML databases, building and exploiting data indices
3. NoSQL databases in applications

1. Creation and feature demonstration of both structured and unstructured data processing, where data may be of various nature.

• Mid-term exam, for which there is only one schedule and, thus, there is no possibility to have another trial.
• One project should be solved and delivered in a given date during a term.

• Mid-term written exam, there is no resit, excused absences are solved by the guarantor.
• The formulation of the data mining task in the prescribed term, excused absences are solved by the assistent.
• The presentation of the project results in the prescribed term, excused absences are solved by the assistent.
• Final exam, The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student. excused absences are solved by the guarantor.

At the end of a term, a student should have at least 50% of points that he or she could obtain during the term; that means at least 20 points out of 40.
Plagiarism and not allowed cooperation will cause that involved students are not classified and disciplinary action can be initiated.