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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gudivada, V.N. Nandigam, J. Yonglei Tao |
| Copyright Year | 2007 |
| Description | Author affiliation: Marshall Univ., Huntington (Gudivada, V.N.) |
| Abstract | 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. |
| File Size | 201215 |
| File Format | |
| ISBN | 9781424410835 |
| ISSN | 01905848 |
| DOI | 10.1109/FIE.2007.4418135 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-10 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Relational databases Spatial databases Data mining Costs Education Application software Web services Query processing Acceleration Business Relational database course Amazon e-commerce service Enhancing student learning Large data sets |
| Content Type | Text |
| Resource Type | Article |
| Subject | Education Computer Science Applications Software |
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