Instructor: Berkay Aydin
Email: baydin2@gsu.edu
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
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%+
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