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

Young Kyoon Suh, Richard T. Snodgrass, Rui Zhang

Research output: Contribution to journalConference articlepeer-review

4 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)
Pages (from-to)1641-1644
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number13
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: Sep 1 2014Sep 5 2014

ASJC Scopus subject areas

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


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