Graduate study programme

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Service Computing and Big Data DRcd1-06-18

ECTS 6 | P 30 | A 0 | L 30 | K 0 | ISVU 149801 | Academic year: 2019./2020.

Course groups

Prikaži sve grupe na predmetu

Course lecturers

MARTINOVIĆ GORAN, Lecturer
BAUMGARTNER ALFONZO, Associate
BAJER DRAŽEN, Associate
ZORIĆ BRUNO, Associate
KURTAGIĆ DINO, Associate
VUČETIĆ VEDRAN, Associate
MALEŠ-GALIĆ IVANA, Lecturer

Goals

Explain [to students] the architectures and principles of service-oriented computing and cloud computing. Introduce students to the requirements and methods for data discovery and analysis. Present the utilisation of service environments, tools, and programming technologies for data analysis in business, research, industry and other application domains.

Conditions for enrollment

Requirements met for enrolling in the study programme

Course description

Service based distributed computing. Service management types and means. Cloud computing. Cloud computing architecture. Defining a platform, infrastructure, application and presentation. User management, reliability, security, authorisation, authentication. Transport formats (XML, JSON). Advanced RESTful web services. Development, testing, placing a service on the market. Implementation properties and the possibility of utilising public clouds (Microsoft Azure, Amazon Web Services, Google App Engine). Big data discovery, storage, handling and processing technologies. Non-relational data, NoSQL and the appropriate technologies. ETL approach. Application of selected statistical and machine learning procedures. Analytical, implementation and learning technologies/tools: R basics, MapReduce, Hadoop, Pig, Hive, Mahout, Azure Machine Learning. Big data analytics in real time. Application in business, scientific and industrial environments, user experiences. Project assignments in cooperation with partner companies.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Obligatory literature

1. 1 Kavis, M.J. Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS) Wiley, 2014.


Pretraži literaturu na:

Recommended additional literature

1. 1 J. Rhoton, R. Haukioja Cloud Computing Explained: Implementation Handbook for Enterprises (2nd Ed.) Recursive Press, 2009.

2. 2 B. Baesens Analytics in a Big Data World: The Essential Guide to Data Science and its Applications Wiley, 2014.

3. 3 B. Ellis Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data Wiley, 2014.

4. 4 EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Wiley, 2015.

5. 5 N. Zumel Practical Data Science with R (1st Ed.) Manning Publications, 2014.

6. 6 F. Provost, T. Fawcett Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking O Reilly Media, 2013.

7. 7 V. Mosco To the Cloud: Big Data in a Turbulent World Paradigm Publishers, 2014.

8. 8 A. Holmes Hadoop in Practice (2nd Ed.) Manning Publications, 2014.

9. 9 M. Barlow Real-Time Big Data Analytics: Emerging Architecture O Reilly, 2013.

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. understand the architecture and principles of service-oriented computing, transport data formats as well as requirements and methods of data analysis in the service environment

2. evaluate the machine learning methods and models to create enhanced algorithmic and software solutions tailored to the service environment

3. create the required architecture of service systems as well as methods and programme methodologies for big data analysis

4. apply the defined architecture of service-oriented computing, approaches and software for data analysis to data obtained from different sources

5. examine the efficiency and applicability of the service-computing environment, methods and programming solutions for a different source data analysis

6. analyse and modify implemented solutions with the aim of improving performance of service-oriented systems with applications



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