CS 6630 : Spatial and Multidimensional Databases

CS 6630: Spatial and Multidimensional Databases

Semester Hours:   3.0
Contact Hours:   3
Coordinator:   Ray Kresman
Text:   Spatial databases- a tour
Authors:   Shekhar and Chawla
Year:   2003

SPECIFIC COURSE INFORMATION

Catalog Description

Introduction to advanced database structures and large datasets. Efficient data structures and related algorithms for spatial, streaming and multi-dimensional and semi-structured datasets. Employs concepts from databases, algorithms, computer graphics and computational geometry. Prerequisites: CS 5620 or permission of instructor.

Course type: ELECTIVE

SPECIFIC COURSE GOALS

  • I am able to store, retrieve and manipulate multidimensional data using advanced data structures such as MX-quad tree, BBD-tree, R-tree, and others.
  • I am able to formulate spatial queries that permit efficient data.
  • I am able to distinguish between various spatial distance metrics.
  • I am able to explain the mechanics of certain algorithms for similarity searching.
  • I am able to use advanced SQL operations to query data warehouses.
  • I am able to explain the nature of streaming data and algorithms for certain problems.
  • I am able to critically evaluate a research literature in the realm of multidimensional, spatial or streaming data.

LIST OF TOPICS COVERED

  1. Introduction
    • Large datasets
    • Spatial data & GIS
    • Streaming data
  2. Graph Theory
    • Elementary graphs
    • Computational geometry
  3. Multidimensional Datasets
    • Transactional data and relational schemas
    • Dimensional models
    • Snowflake schemas
    • Data warehousing & SQL
  4. Spatial Datasets
    • Representation
    • Access methods
    • Trees: R-tree, Kd-tree, quad-tree, etc.
    • Performance tradeoffs
  5. Data Storage and Manipulation
    • Spatial Object types
    • Spatial queries & operations
    • Similarity search/methods
    • Spatial algebra
  6. Streaming Data
    • Sample problem: sampling, cardinality/moments estimation
    • Clustering & space filling cures
    • Approximation algorithms
  7. Performance
    • Spatial indices
    • Clustering & space filling curves
    • Data quality and metrics
  8. Mining
    • Association rules
    • Continuous space and spatial co-location
    • Spatial autocorrelation

Updated: 12/17/2025 05:22PM