Faculty of Technical Sciences

Subject: Big data in infrastructure systems (17.ESI112)

General information:
 
Category Professional-applicative
Scientific or art field Applied Computer Science and Informatics
ECTS 6

Introducing students with the advanced principles and techniques of warehousing, analysis and managing large sets of data (Big Data) and their application in infrastructure systems. Students should apply acquired knowledge in anaysis, study and solving real problems.

Acquisition of modern knowledge of warehousing and algorithms for processing large amounts of data, with emphasis on application in infrastructure systems. The student is trained to analyze, study and solve real problems using the acquired knowledge.

Definition, types and characteristics of infrastructure systems. The importance of smart analytics and infrastructure (smart cities, smart homes, smart governance, etc.). Technologies Hadoop and Spark. Warehousing of large amounts of frequency and unstructured data. Characteristics, advantages and disadvantages of NoSQL databases. Virtualization of databases. Manage large databases in cloud. MapReduce and HPCC parallel and distributed data processing. Analysis of data, their connections and possibilities of reducing dimensionality. Application of the spectral graph theory in the analysis of large amounts of data. Machine learning algorithms on large datasets. Visualization of data. Decision support systems and report creation.

Lectures. Computer practice. Consultations. The student is obliged to independently do the project and write a seminar paper.

Authors Title Year Publisher Language
Greg Schulz Software-Defined Data Infrastructure Essentials: Cloud, Converged, and Virtual Fundamental Server Storage I/O Tradecraft 2017 CRC Press English
J. Leskovac, A. Rajaraman, J. D. Ullman Mining of Massive Datasets 2010 Cambridge University Press English
M. Magdon-Ismail, Y. AbuMostafa Learning from Data 2012 AMLBook English
Michael Manoochehri Data Just Right: Introduction to Large-Scale Data & Analytics 2014 Addison-Wesley English
Marz, N., Warren, J. Big Data : Principles and best practices of scalable realtime data systems 2015 Manning Publications, New York English
Sean Gerrish How Smart Machines Think 2018 MIT Press English
Course activity Pre-examination Obligations Number of points
Project Yes Yes 50.00
Theoretical part of the exam No Yes 30.00
Term paper Yes Yes 20.00
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Prof. Kupusinac Aleksandar

Full Professor

Lectures

Asst. Prof. Vrbaški Dunja

Assistant Professor

Computational classes

Assistant - Master Samardžić Bojana

Assistant - Master

Computational classes

Assistant - Master Mijatov Vanja

Assistant - Master

Computational classes

Faculty of Technical Sciences

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© 2024. Faculty of Technical Sciences.