Enhancing student learning in database courses with large data sets

• Main Authors: Gudivada, V.N., Nandigam, J., Yonglei Tao
• Format: Conference Proceeding
• Language: English
• Subjects: Acceleration; Amazon e-commerce service; Application software; Business; Costs; Data mining; Education; Enhancing student learning; Large data sets; Query processing; Relational database course; Relational databases; Spatial databases; Web services
• ISSN: 0190-5848; 2377-634X
• DOI: 10.1109/FIE.2007.4418135

Rapidly increasing storage device capacities at ever decreasing costs have resulted in mushrooming of publicly available large data sets on the Web. In this paper, we describe a novel approach to teaching relational database course by using such data repositories. We demonstrate our approach using the Amazon.com product database, though the approach is generic and is applicable to other data repositories. The Amazon database is supposedly the largest product database ever in existence. We have used the Amazon Web Services API and .NET/C# application to extract a subset of the product database to enhance student learning in a relational database course. This realistic data served various activities of the course and provided a rich backdrop to demonstrate more interesting features of SQL and Oracle cost-based query optimization. Central to the course is a semester-long team project. We discuss the details of data extraction from Amazon.com, conceptual and logical data modeling, logical and physical database design, database creation and data loading, database querying, and database application development.