Geospatial Database Management System

Syllabus

1. Introduction (4 hours)
   1. Database Management System (DBMS): Terms and terminologies; Application; Comparison with other data technologies
   2. Geospatial database technology
   3. Components and function of DBMS; Interaction with DBMS
2. Data Models and Database Languages (4 hours)
   1. Overview of data models
   2. Entity relationship (ER) model, relational model and unified modeling language (UML)
   3. Database language: Types, constraints, keys and design issues
   4. ER diagram
3. Relational Data Model (4 hours)
   1. Terminologies and need of relational data model
   2. Constraints and keys of relational database
   3. Relational algebra and derived operations
4. Structured Query Language (SQL) (4 hours)
   1. Background and basic structure
   2. Set operation; Aggregate functions; Null values
   3. Nested sub queries and views
   4. Modification of database; Joined relationship
   5. Data-definition language
5. Spatial Database Technology (6 hours)
   1. Spatial DBMS (SDBMS)
   2. Values of SDBMS
   3. Basics of spatial taxonomy and data types (Vector and Raster)
   4. Data mining
6. Spatial Concepts and Data Models (6 hours)
   1. Spatial information models: Field and object based
   2. Spatial data formats and exchange standards
   3. Spatial operations: Set oriented; Topologies; Directional; Metric space; Euclidean
   4. ER model with spatial notion
   5. Object oriented data modeling with UML
   6. Comparison of ER model and UML
7. Spatial Query Language (5 hours)
   1. Concept on spatial query language
   2. Spatial data standards
   3. SQL for spatial databases; OGIS standard for extending SQL
   4. Object-relational SQL; Object-relationship schema
8. Computational Geometry (6 hours)
   1. Algorithms: Concept, analysis, optimality and data structure
   2. Useful algorithm strategies
   3. Polygon partitioning
   4. Algorithm for spatial database
9. Spatial Storage and Optimization (6 hours)
   1. Storage; Disks and files
   2. Disk geometry and implication
   3. Buffer manager
   4. File structures
   5. Clustering
   6. Spatial indexing: R-tree; Quad-tree; Grid indexing
   7. Storage optimization (Cost models)

Practicals

1. Building ER model
2. Development of UML
3. Design schema for cadastral survey
4. Set operation; Aggregate functions; Null values in SQL
5. Nested sub queries and views in SQL
6. Modification of database; Joined relationship in SQL
7. Building a Postgres and PostGIS database
8. Working with PostGIS spatial functions
9. Spatial query exercise in Postgres and PostGIS
10. Using PostGIS in web application