Geospatial Databases

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. Object relations. ISO/IEC SQL/Spatial. Recursive queries. Semistructured data model. GML. XQuery. Geometrical and topological queries. Spatial index structures: quadtree, 2D tree, R-tree, R+ tree. Big data paradigm and MapReduce programming model. HiveQL: data definition, data manipulation, queries, spatial user-defined functions (UDF).

General Competencies

The objectives of the course are to give the students the knowledge theory and practice of geospatial information systems.Upon the completition of the course, students will be qualified to do following: 1. good understanding of geospatial modelling concepts; 2. ability to design geospatial databases; 3. practical skills to design and develop GIS applications.

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

theory with application examples

Other Forms of Group and Self Study

Students are divided into groups of 2 or 3. Each group is assigned a separate data set. By completing a project using an assigned data set, students exhibit relevant practical skills and application of learned theoretical concepts in the area of geospatial databases.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Class participation 0 % 5 % 0 % 0 %
Seminar/Project 0 % 25 % 0 % 0 %
Attendance 0 % 5 % 0 % 0 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 40 %
Exam: Written 0 % 50 %
Exam: Oral 50 %

Week by Week Schedule

  1. Introduction to the course. Intoruction to GIS and geospatial databases. Forming project groups.
  2. Spatial awareness and spatial topology. Geospatial modelling and models. Field model. Object model.
  3. Introduction to apstract geospatial data types (GeoADT). 9 intersection model.
  4. Dimensionally extended 9 intersection model. Topological operators. Geometry set operators. Specific spatial operators.
  5. OGC simple feature access part1 common architecture (ISO/WD 19125-1)
  6. OGC simple feature access part 2: SQL option (ISO 19125-2:2004).
  7. GeoADT in Object-Relational databases.
  8. Midterm exam
  9. Discussion about the midterm exam and student projects. Reference systems and cartographic projection.
  10. Geography Markup Language (GML).
  11. MapReduce programming model. HiveQL: data definition, data manipulation, queries, spatial user-defined functions (UDF).
  12. Spatial data indexing. Grid, quadtree, R-tree and GIST indexing structures.
  13. Student project presentations, part 1
  14. Student project presentations, part 2
  15. Final exam

Study Programmes

University graduate
Software Engineering and Information Systems (profile)
Specialization Course (2. semester)

Prerequisites

Literature

Zdravko Galić (2006.), Geprostorne baze podataka, Golden Marketing - Tehnička knjiga
M. Worboys, M. Duckham (2004.), GIS: A Computing Perspective, CRC Press
E. Capriolo, D. Wampler and J. Rutherglen (2012.), Programming Hive, O'Reilly

General

ID 35211
  Summer semester
4 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Exercises
0 Laboratory exercises
0 Project laboratory

Grading System

87,5 Excellent
75 Very Good
62,5 Good
50 Acceptable