• DocumentCode
    2593963
  • Title

    Enhancing student learning in database courses with large data sets

  • Author

    Gudivada, Venkat N. ; Nandigam, Jagadeesh ; Tao, Yonglei

  • Author_Institution
    Marshall Univ., Huntington
  • fYear
    2007
  • fDate
    10-13 Oct. 2007
  • 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.
  • Keywords
    SQL; computer science education; data models; educational courses; query processing; relational databases; teaching; very large databases; Amazon database; Oracle; SQL; data extraction; data loading; data modeling; data repository; database application development; database creation; database design; database querying; large data sets; product database; query optimization; relational database course; student learning; teaching; Acceleration; Application software; Business; Costs; Data mining; Education; Query processing; Relational databases; Spatial databases; Web services; Amazon e-commerce service; Enhancing student learning; Large data sets; Relational database course;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. FIE '07. 37th Annual
  • Conference_Location
    Milwaukee, WI
  • ISSN
    0190-5848
  • Print_ISBN
    978-1-4244-1083-5
  • Electronic_ISBN
    0190-5848
  • Type

    conf

  • DOI
    10.1109/FIE.2007.4418135
  • Filename
    4418135