Faculty of Technical Sciences

Subject: Big data management and analysis (17.EK552)

General information:
 
Category Theoretical-methodological
Scientific or art field Telecommunications and Signal Processing
ECTS 5

Introducing students with computer and network infrastructure for storage of large amounts of data, great heterogeneity and large arrival speed. Acquiring knowledge and mastering practical skills in apply different algorithms for analyzing and managing large sets of data (Big Data).

Identifying value in data and discovering knowledge from data.Construction of physical and virtual storage for storing large amounts of data. Students will be able to use the distributed file systems and MapReduce as a tool for creating parallel algorithms that succeed on big data. Understanding and implementation of the algorithms for big data management and combining and evaluating algorithms for mining big data sets.

Data warehousing. Distributed file systems (Hadoop, Spark). Virtual warehouses and communications. Database virtualization. Management of large databases on cloud. MapReduce program model for distributed data processing. Data Ssearches (Similar Samples, Frequency Sample Samples). Data in the form of graphs, link analysis, local and global topological attributes. Machine learning algorithms for big data. Data visualization.

Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, students' competitions.

Authors Title Year Publisher Language
Tom White Hadoop: Definitive Guide, 4 2015 OReilly Media English
J. Leskovac, A. Rajaraman, J. D. Ullman Mining of Massive Datasets 2010 Cambridge University Press English
Michael Manoochehri Data Just Right: Introduction to Large-Scale Data & Analytics 2014 Addison-Wesley English
Greg Schulz Software-Defined Data Infrastructure Essentials: Cloud, Converged, and Virtual Fundamental Server Storage I/O Tradecraft 2017 CRC Press English
Tom Clark Storage Virtualization: Technologies for Simplifying Data Storage and Management: Technologies for Simplifying Data Storage and Management 2005 Addison Wesley English
Course activity Pre-examination Obligations Number of points
Homework Yes Yes 5.00
Homework Yes Yes 5.00
Homework Yes Yes 5.00
Theoretical part of the exam No Yes 30.00
Homework Yes Yes 5.00
Project Yes Yes 50.00
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Prof. Lončar-Turukalo Tatjana

Full Professor

Lectures
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Assoc. Prof. Škorić Tamara

Associate Professor

Lectures
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Asst. Prof. Suzić Siniša

Assistant Professor

Practical classes

Assistant - Master Lazić Ivan

Assistant - Master

Practical classes
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Asst. Prof. Suzić Siniša

Assistant Professor

Computational classes

Assistant - Master Lazić Ivan

Assistant - Master

Computational classes

Faculty of Technical Sciences

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© 2024. Faculty of Technical Sciences.