460-4101/02 – Algorithms for Big Data (ARD)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorprof. RNDr. Václav Snášel, CSc.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year2Semesterwinter
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
PLA06 doc. Ing. Jan Platoš, Ph.D.
SNA57 prof. RNDr. Václav Snášel, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2
Combined Graded credit 10+0

Subject aims expressed by acquired skills and competences

Evaluation and interpretation of information obtained from the measured and recorded Gig Data from the practice. Methods of data mining, mathematical, statistical and logical methods for solving this class of research and practical problems.

Teaching methods

Lectures
Tutorials

Summary

Students, during the course, are introduced to the basic approaches, methods and algorithms from big data processing. The lectures will provide the necessary amount of theory so that it can be applied during the individual work of the students on the tutorials. Tutorials will provide space for discussing the problems, showing practical tasks and exercising on simple examples.

Compulsory literature:

Fatos Xhafa, Leonard Barolli, Admir Barolli, Petraq Papajorgji.Modeling and Processing for Next-Generation Big-Data Technologies: With Applications and Case Studies. Springer 2014. Robinson, Ian; Webber, Jim; Eifrem, Emil.Graph Databases. O'Reilly Media. 2014. O'Reilly Radar Team. Big Data Now: Current Perspectives from O'Reilly Radar, O'Reilly Media. 2014.

Recommended literature:

O'Reilly Radar Team. Big Data Now: Current Perspectives from O'Reilly Radar, O'Reilly Media. 2014.

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

Additional requirements are not placed on the student.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Modelling in Big data Behavior Detection Metric and topological properties Dimension reduction methods Log analysis Visualization of Data Clustering on Big Data Machine Learning NoSQL database Graph database

Conditions for subject completion

Full-time form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100  51
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 2 Optional study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava Compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava Optional study plan
2018/2019 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 2 Optional study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 2 Optional study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (1801T064) Information and Communication Security P English Ostrava 2 Optional study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner