Groups of collaborating people in many diverse fields of endeavour face the challenge of characterizing or mining information from a large database. In science, the database might contain astronomy or Human Genome data. In business, the database might contain customer purchase or supply chain data. In sociology, the database may contain census or demographic data. Wherever large databases exist, regardless of their exact contents, there are groups of people endeavouring to understand that data. Typically, this is done with individual SQL queries that summarize certain aspects of the data with little support for collaboration between people. In this paper, we present a web-based tool for collaborative exploration and characterization of large relational databases. We demonstrate that our tool can be used to avoid repetition of expensive long-running queries, can allow users to learn from the mistakes of others and can allow users to build upon one another’s successes.
This lack of collaboration has three main drawbacks. First, running a query over a large dataset can take hours or even days and users often run many of the same basic SQL queries to summarize basic aspects of the dataset. With support for collaboration, users could benefit from answers obtained by other users. Second, as users move beyond basic queries, it can often take several tries to write a query that accomplishes the intended purpose. With support for collaboration, users could learn from each other’s mistakes. Third, two users may be characterizing a similar aspect of the data, but are unable to help one another modify their queries. With support for collaboration, users could build upon one another’s work in real-time.