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Course info
KIV / DB2
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Course description
Department/Unit / Abbreviation
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KIV
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DB2
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Database Systems 2
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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23 / -
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8 / -
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1 / 4
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
Yes
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Fundamental course |
Yes
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Fundamental theoretical course |
Yes
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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KIV/DB1 and KIV/DBA
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Courses depending on this Course
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KIV/PMZD, KIV/SZD, KIV/VS, KIV/ZDSZ, KMA/KAN
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aim of course is to make sense of post-relational database systems (temporal, spatial, deductive, and multimediální). Various systems and their implementation techniques are discussed.
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Requirements on student
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Semester work + practical verification
Control test
Combined exam
The deadline for obtaining credit is May 9, 2024.
Due to the continuous updating of the course, in order to obtain credit when re-enrolling the course (see SZŘ Article 24, paragraph 3), the consent of the guarantor of the course is required.
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Content
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Standards in the field of database technologies. Relational data model. Overview of alternative data models and database systems.
Big Data issues, principles of Big Data management - CAP theorem, distribution, scaling, replication, transactions in a distributed environment.
Overview and classification of database systems and technologies for Big Data management and processing.
Distributed file systems. MapReduce.
NoSQL database - BASE vs. ACID.
Database key / value, document, columnar database.
Graph data and graph databases
Data with multiple models. Multi-model database. Polystores
SQL language in the Big Data environment. NewSQL database.
Trends in the development of modern databases, hybrid databases, field databases.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Pokorný J., Valenta M. Databázové systémy. Praha, 2020. ISBN 978-80-01-06696-6.
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Basic:
Elmasri R., Navathe S.B. Fundamentals of Database Systems. Pearson, 2017. ISBN 9780133971118.
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Recommended:
Kroenke, David M. Database Processing: Fundamentals, Design, and Implementation: International Edition, 10/E. Pearson Higher Education, 2006. ISBN 9780131971042.
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Recommended:
Rob, P., Coronel, C. Database Systems: Design, Implementation And Management. Course Technology Ptr, 2006. ISBN 9781418835934.
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Recommended:
Sokolowsky, Peter; Peterka, Jiří; Pokorný, Jaroslav. Distribuované databázové systémy. 1. vyd. Praha : Academia, 1992. ISBN 80-200-0442-4.
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Recommended:
Bell D., Grimson J. Distributed Database Systems. Addison Wesley, 1992.
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Recommended:
Klečková J. Uživatelské rozhraní databázových systémů. Plzeň, 1996.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for comprehensive test (10-40)
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11
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Preparation for an examination (30-60)
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40
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Graduate study programme term essay (40-50)
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40
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Contact hours
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65
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Total
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156
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
demonstrate knowledge of the fundamentals of database technology |
demonstrate knowledge of the theory of relational data model |
explain the formalization of relational database design |
understand the principles of data integrity, transactions and SQL language |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
suggest database system small and medium scale |
design and implement a simpler standalone application |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
Explain and illustrate methods of analysis, design and creation of database systems working with large data integrated from many communicating components. |
Identify post-relational DB systems in selected categories and propose the implementation of these systems. |
vysvětlit principy relačních databází, datové integrity a základních SQL příkazů, popsat postupy datového modelování |
Skills - skills resulting from the course: |
Analyze and standard requirements and situation and propose solutions built on trivial modification of existing procedures and components. |
design and optimize extensive data model |
select and effectively use methods and technologies for the processing, analysis and representation of large structured and unstructured data |
create and administer Oracle database |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Test |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Test |
Individual presentation at a seminar |
Seminar work |
Competences - competence achieved by taking this course are verified by the following means: |
Project |
Skills demonstration during practicum |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Practicum |
Students' portfolio |
Skills - the following training methods are used to achieve the required skills: |
Individual study |
Competences - the following training methods are used to achieve the required competences: |
Lecture |
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