AZDBLab: A laboratory information system for large-scale empirical DBMS studies

Young Kyoon Suh, Richard Thomas Snodgrass, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


In the database field, while very strong mathematical and engineering work has been done, the scientific approach has been much less prominent. The deep understanding of query optimizers obtained through the scientific approach can lead to better engineered designs. Unlike other domains, there have been few DBMS-dedicated laboratories, focusing on such scientific investigation. In this demonstration, we present a novel DBMS-oriented research infrastructure, called Arizona Database Laboratory (AZDBLab), to assist database researchers in conducting a large-scale empirical study across multiple DBMSes. For them to test their hypotheses on the behavior of query optimizers, AZDBLab can run and monitor a large-scale experiment with thousands (or millions) of queries on different DBMSes. Furthermore, AZDBLab can help users automatically analyze these queries. In the demo, the audience will interact with AZDBLab through the stand- alone application and the mobile app to conduct such a large-scale experiment for a study. The audience will then run a Tucson Timing Protocol analysis on the finished experiment and then see the analysis (data sanity check and timing) results.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Number of pages4
Publication statusPublished - 2014


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Suh, Y. K., Snodgrass, R. T., & Zhang, R. (2014). AZDBLab: A laboratory information system for large-scale empirical DBMS studies. In Proceedings of the VLDB Endowment (13 ed., Vol. 7, pp. 1641-1644). Association for Computing Machinery.