Collaborative Query Management:
    Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly.
   This mode of interaction, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In these new settings, data management systems must provide powerful query management capabilities.
   Therefore, we are building a Collaborative Query Management System (CQMS) that provides capabilities that range from query browsing to automatic query recommendations.
Nodira Khoussainova, Magdalena Balazinska, and Dan Suciu. In VLDB 2012
Nodira Khoussainova, YongChul Kwon, Wei-Ting Liao, Magdalena Balazinska, Wolfgang Gatterbauer, and Dan Suciu. In SSDBM 2011
Nodira Khoussainova, YongChul Kwon, Magdalena Balazinska, and Dan Suciu. In PVLDB Vol 4 Number 1 - VLDB 2011
Nodira Khoussainova, Magdalena Balazinska, Wolfgang Gatterbauer, YongChul Kwon, and Dan Suciu. In CIDR 2009 - Perspectives

The CQMS project is partially supported by NSF CAREER award IIS-0845397, NSF grant IIS-0627585, NSF grant IIS-0713123, NSF CRI grant CNS-0454425, Balazinska's Microsoft Research New Faculty Fellowship, a Google Research Award, a gift from Amazon, and an HP Labs Innovation Research Award.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Collaborative Query Management
                Magdalena Balazinska
                 Dan Suciu
                 Wolfgang Gatterbauer
                 Nodira Khoussainova
                 YongChul Kwon