What Teaching Databases Taught Us about Researching Databases: Extended Talk Abstract

Jun Yang, Amir Gilad, Yihao Hu, Hanze Meng, Zhengjie Miao, Sudeepa Roy, Kristin Stephens-Martinez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Declarative querying is a cornerstone of the success and longevity of database systems, yet it is challenging for novice learners accustomed to different coding paradigms. The transition is further hampered by a lack of query debugging tools compared to the plethora available for programming languages. The paper samples several systems that we build at Duke University to help students learn and debug database queries. These systems have not only helped scale up teaching and improve learning, but also inspired interesting research on databases. Furthermore, with the rise of generative AI, we argue that there is a heightened need for skills in scrutinizing and debugging AI-generated queries, and we outline several ongoing and future work directions aimed at addressing this challenge.

Original languageEnglish
Title of host publicationProceedings of the 3rd ACM SIGMOD International Workshop on Data Systems Education
Subtitle of host publicationBridging Education Practice with Education Research, DataEd 2024
EditorsDaphne Miedema, Sourav S. Bhowmick, Michael Liut
Pages1-6
Number of pages6
ISBN (Electronic)9798400706783
DOIs
StatePublished - 9 Jun 2024
Event3rd ACM SIGMOD International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd 2024 - Santiago, Chile
Duration: 9 Jun 20249 Jun 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd ACM SIGMOD International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd 2024
Country/TerritoryChile
CitySantiago
Period9/06/249/06/24

Keywords

  • Database Education
  • Query Debugging
  • Query Verification
  • Relational Algebra
  • SQL

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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