Welcome to the home page of the Data Management Research Group at Brown University's Department of Computer Science. Our research group is focused on a wide-range of problem domains for database management systems, including analytical (OLAP), transactional (OLTP), and scientific workloads.

Latest News

Accepted Papers Q1/2016

March 25th, 2016
  • Demo Paper “Making the case for Query-by-Voice with EchoQuery“  accepted to SIGMOD 2016
  • Vision Paper “The End of Slow Networks: It’s Time for a Redesign” accepted to VLDB 2016
  • Research Paper “Making Distributed Transactions Scale” accepted to NeDBDay 2016
  • Research Paper “Estimating the Impact of Unknown Unknowns on Aggregate Query Results” accepted to SIGMOD 2016

Independent Study Request (Spring 2016)

January 27th, 2016

Please fill out this form if you want to do an independent study with us. Topics include but are not limited to:

  • Scalable Data Management on RDMA (i-Store)
  • Visual Data Exploration (VizDom)
  • Scalable Machine Learning (TupleWare)


SIGMOD 2016 Accepted Paper

November 22nd, 2015

The Brown Data Management Group has the following paper in SIGMOD 2016:

  • Estimating the Impact of Unknown Unknowns on Aggregate Query Results
       Yeounoh Chung, Michael Lind Mortensen, Carsten Binnig, Tim Kraska

    It is common practice for data scientists to acquire and in- tegrate disparate data sources to achieve higher quality re- sults. But even with a perfectly cleaned and merged data set, two fundamental questions remain: (1) is the integrated data set complete and (2) what is the impact of any unknown (i.e., unobserved) data on query results?
    In this work, we develop and analyze techniques to esti- mate the impact of the unknown data (a.k.a., unknown unknowns) on simple aggregate queries. The key idea is that the overlap between different data sources enables us to estimate the number and values of the missing data items. Our main techniques are parameter-free and do not assume prior knowledge about the distribution. Through a series of experiments, we show that estimating the impact of unknown unknowns is invaluable to better assess the results of aggregate queries over integrated data sources.