Geospatial Databases

Data is displayed for academic year: 2023./2024.

Lecturers

Prof.
Igor Mekterović
Assoc. Prof.
Ljiljana Brkić

Associate Lecturers

Asst. Prof.
Zdravko Galić

Course Description

Database management systems. Geospatial databases. Geospatial abstract data types. Geospatial data modeling. Topological concepts. 9-IM. DE9-IM. Metric space. Euclidiean space. Algebraic specification of abstract data types. Spatial reference systems (geoid, reference ellipsoid, map projections). Object-relational data model. User-defined data types. ISO/IEC SQL/Spatial. Recursive queries. Spatial index structures: quadtree, 2D tree, R-tree, R+ tree. Topology and topological data structures. Semistructured data model. GML and GeoJSON. XQuery. Geometrical and topological queries. Distributed SQL spatial databases. Big data and MapReduce programming model.

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the Profile (1. semester)
Elective Courses (1. semester) (3. semester)

Learning Outcomes

  1. define abstract geospatial data types
  2. define and apply relevant topological, geometry and set operators
  3. model and implement geospatial data within a object-relational or semi-structured data model
  4. design and generate GML schema and GML document for a given data SET
  5. model and implement geospatial data within a big data management paradigm
  6. write efficient queries ove geospatial data
  7. apply and understand methods of indexing geospatial data
  8. design and implement spatial data within a big data context

Forms of Teaching

Lectures

Independent assignments

Work with mentor

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Class participation 4 % 5 % 0 % 0 %
Seminar/Project 20 % 25 % 0 % 0 %
Attendance 4 % 5 % 0 % 0 %
Mid Term Exam: Written 11 % 25 % 0 %
Final Exam: Written 26 % 65 %

Week by Week Schedule

  1. Fundamental spatial concepts
  2. Spatial reference systems
  3. Spatio-temporal conceptual modeling
  4. Indexing spatial data
  5. Computational geometry
  6. Spatial algorithms
  7. Multidimensional and metric data structures
  8. Midterm exam
  9. Spatial data analysis
  10. Spatial data analysis, Spatial and spatio-temporal visualization
  11. Spatial and spatio-temporal visualization
  12. Web mapping
  13. Web Map and Web Feature services
  14. GeoSPARQL
  15. Final exam

Literature

Zdravko Galić (2006.), Geprostorne baze podataka, Golden Marketing - Tehnička knjiga
Michael F. Worboys, Matt Duckham (2004.), GIS, CRC Press
Tom White (2010.), Hadoop - The Definitive Guide, "O'Reilly Media, Inc."

For students

General

ID 222525
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

87.5 Excellent
70.0 Very Good
62.5 Good
50.0 Sufficient

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