Information Systems and Big Data

Објавено: October 23, 2019
  1.    Course Title Information Systems and Big Data
  2.    Code 3ФЕИТ07Л009
  3.    Study program KTI, TKII
  4.    Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
  5.    Degree (first, second, third cycle) First cycle
  6.    Academic year/semester III/6, IV/8   7.    Number of ECTS credits 6.00
  8.    Lecturer Dr Hristijan Gjoreski
  9.    Course Prerequisites

10.    Course Goals (acquired competencies):  Working with distributed databases. Fragmentation of databases. Working with large data. Upon completion, the student will be able to create data fragments and create, analyze and handle large data.

11.    Course Syllabus: Introduction to large databases. Distribution of data. Concepts, advantages and disadvantages of distributed data. Creating Distribution. Distribution of data by dividing by selection (horizontally). Distribution of data by means of projection split (vertically). Access and processing of issues in data distribution. Designing DB according to data distribution. Adjustment of DB according to requirements. Ways to optimize DB according to the queries. Optimizing by location. Introduction to Data Warehouses. Defining and Concept of Warehouses.  Work with OLAP and OLTP. Types of data warehouses. Modeling warehouses. Star and snowflake pattern. Object Database. Pure-object databases. Object model. Object-relational databases. SQL mapping in ORBP. Data mining of large data. Algorithms of machine learning and AI. Large data analysis. Related large data structures. Analysis by graphs.
12.    Learning methods:  Theoretical and practical (laboratory) classes
13.    Total number of course hours 2 + 2 + 1 + 0
14.    Distribution of course hours 180
15.    Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16.    Other course activities 16.1. Projects, seminar papers 25
16.2. Individual tasks 20
16.3. Homework and self-learning 60
17.    Grading 17.1. Exams 10
17.2. Seminar work/project (presentation: written and oral) 10
17.3. Activity and participation 0
17.4. Final exam 80
18.    Grading criteria (points) up to 50 points     5 (five) (F)
from 51 to 60 points     6 (six) (E)
from 61 to 70 points     7 (seven) (D)
from 71 to 80 points     8 (eight) (C)
from 81 to 90 points     9 (nine) (B)
from 91 to 100 points   10 (ten) (A)
19.    Conditions for acquiring teacher’s signature and for taking final exam Practical (laboratory) exercises
20.  Forms of assessment Two partial exams during the semester lasting 120 minutes each or one final written exam in an appropriate exam session lasting 120 minutes. Evaluation of laboratory exercises. Possibility to do a seminar work that will be part of the evaluation
21.    Language Macedonian and English
22.    Method of monitoring of teaching quality Evaluation of the curriculum using tests and quizzes
23.    Literature
23.1. Required Literature
No. Author Title Publisher Year
1 Ralph Stair, George Reynolds Fundamentals of information systems Course Technology 2015
2 Anand Jarajaman, Jerffrey Ullman Mining of massive datasets Cambridge 2011
3 Jimmy Lin, Chris Dyer Data-Intensive Text Processing with MapReduce Morgan and Claypool 2010