Abstract Leveraging Usage History to Enhance Database Usability

More so than ever before, large datasets are being collected and analyzed throughout a variety of disciplines. Examples include social networking data, software logs, scientific data, web clickstreams, sensor network data, and more. As such, there are a wide range of users interacting with these large datasets, ranging from scientists, to data analysts, to sociologists, to market researchers. These users are experts in their domain and understand their data extensively, but are not database experts. Database systems are scalable and efficient, but are notoriously difficult to use. In this work, we aim to address this challenge, by leveraging usage history. From usage history, we can extract knowledge about the multitude of users ’ experiences with the database. Consequently, this knowledge allows us to build smarter systems that better cater to the users ’ needs. We address different aspects of the database usability problem and develop three com-plementary systems. First, we aim to ease the query formulation process. We build the SnipSuggest system, which is an autocompletion tool for SQL queries. It provides on-the-go, context-aware assistance in the query composition process. The second challenge w