CSC8713 Spatial and Scientific Databases

Administrative Info

Instructor: Berkay Aydin 


Course Webpage: this webpage or can be reached via iCollege

Office Location:  25 Park Pl NE - Room 740 

Credit Hours:   4.0 hours

Pre-requisites:   CS 4710/6710 (or equivalent) with grade of “C” or higher

Course Overview

This course presents, in detail, following advanced concepts of database systems: spatial data storage and retrieval, multi-dimensional indexing techniques, advanced data modeling techniques, data warehouses, and a few contemporary areas of applied database-related research. The lectures are designed to provide graduate students with sufficient foundation to conduct their own, but supervised research in the field of databases at the graduate-student level. Students will gain hands-on experience on the chosen aspect of database technology through completion of a graduate research project, which will be reviewed by their peers at the end of the semester. In the first part of the course the following spatial and temporal data management components will be introduced: spatial concepts, data types and modeling, spatial query languages, indexing, and query optimization. In the second part of the course selected aspects of popular data warehousing and scientific data management concepts will be presented. These include: data warehousing,  unstructured data management (storage, processing and retrieval) and mechanisms for storage and processing of imprecise data. The last part of the course will be devoted to students’ individual research, conducted during work on graduate projects, which are going to be developed in the second half of the semester. During the third part of the course the students are encouraged to extend the presented material by their own studies and by the development of projects meeting their own research interests and the gathered database expertise. The work on projects will be closely supervised by the instructor of the course.


At the end of the course, students should be able to:

-    Understand advanced concepts of spatial and spatio-temporal databases

-    Understand advanced concepts of popular data warehousing methodologies and scientific databases and data integration

-    Develop a scientific project that involves managing, integration, and/or processing of large-scale data repositories

Present their findings on the conducted research in the form of a technical presentation and a scientific publication 


Students are expected to have at least moderate programming skills in Python, advanced programming skills in SQL. They are also expected to be well-versed in database design, modeling, and basic access methods (such as hashing, B/B+ trees etc.).  


In order to get a passing grade in this course, students are expected to :

 1- Do 3 HW assignments      30%+

 2-  Midterm Exams                33%+

       Exam 0 (3%) 

       Exam 1(10%) 

       Exam 2 (10%)

       Exam 3 (10%)

3-Project                                  40%+

    Implementation (10%) 

     Final Report (15%) 

     Presentations (15%) 

Total                                       100%+

Course Outline

Introduction to Databases

Introduction to Spatial Databases

Spatial and Temporal Data Modeling

Spatial Query Languages

Spatial and Temporal Storage and Indexing

Data Warehousing

Multimedia Data Modeling and Indexing

High Dimensional Data Indexing

Modeling and Querying Graph Data

Unstructured Data Management