Course detail

Data Storage and Preparation

FIT-UPAAcad. year: 2020/2021

The course
focuses on modern database systems as typical data sources for knowledge
discovery and further on the preparation of data for knowledge discovery.
Discussed are extended relational (object-relational, with support for working
with XML and JSON documents), spatial, and NoSQL database systems. The
corresponding database model, the way of working with data and some methods of
indexing are explained. In the context of the knowledge discovery process,
attention is paid to the descriptive characteristics of data and visualization
techniques used to data understanding. In addition, approaches to solving
typical data pre-processing tasks for knowledge discovery, such as data
cleaning, integration, transformation, reduction, etc. are explained.
Approaches to information extraction from the web are also presented and
several real case studies are presented. As a part of the course, students solve a
project focused on ...

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Lecture materials (slides, scripts, etc.)
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 p., ISBN 1-558-60677-7
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1
Skiena, S.S.: The Data Science Design Manual. Springer, 2017, 445 p., ISBN 978-3-319-55443-3.
Shekhar, S., Chawla, S.: Spatial Databases: A Tour, Prentice Hall, 2002/2003, 262 p., ISBN 0-13-017480-7
Gaede, V., Günther, O.: Multidimensional Access Methods, ACM Computing Surveys, Vol. 30, No. 2, 1998, pp. 170-231.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • 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.

Exam prerequisites:
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.

Language of instruction

Czech

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

  • 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.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NGRI , any year of study, winter semester, 5 credits, compulsory
    specialization NSEC , any year of study, winter semester, 5 credits, compulsory
    specialization NEMB , any year of study, winter semester, 5 credits, compulsory
    specialization NHPC , any year of study, winter semester, 5 credits, compulsory
    specialization NISY , any year of study, winter semester, 5 credits, compulsory
    specialization NMAT , any year of study, winter semester, 5 credits, compulsory
    specialization NVER , any year of study, winter semester, 5 credits, compulsory
    specialization NADE , 1. year of study, winter semester, 5 credits, compulsory
    specialization NBIO , 1. year of study, winter semester, 5 credits, compulsory
    specialization NNET , 1. year of study, winter semester, 5 credits, compulsory
    specialization NVIZ , 1. year of study, winter semester, 5 credits, compulsory
    specialization NCPS , 1. year of study, winter semester, 5 credits, compulsory
    specialization NISD , 1. year of study, winter semester, 5 credits, compulsory
    specialization NIDE , 1. year of study, winter semester, 5 credits, compulsory
    specialization NMAL , 1. year of study, winter semester, 5 credits, compulsory
    specialization NSEN , 1. year of study, winter semester, 5 credits, compulsory
    specialization NSPE , 1. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  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.

Fundamentals seminar

6 hours, compulsory

Teacher / Lecturer

Syllabus

DEMO excercises

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

Exercise in computer lab

6 hours, compulsory

Teacher / Lecturer

Syllabus

  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

Project

14 hours, compulsory

Teacher / Lecturer

Syllabus

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