Highly interactive query interfaces have become a popular tool for ad-hoc data analysis and exploration, posing a new kind of workload to the underlying data infrastructure. Compared with traditional systems that are optimized for throughput or batched performance, ad-hoc and interactive data exploration systems focus more on user-centric interactivity, which raises a new class of performance challenges. Further, with the advent of new interaction devices~(e.g., touch, gesture) and different query interface paradigms~(e.g., sliders), maintaining interactive performance becomes even more challenging. Thus, when building interactive data systems, there is a clear need to articulate the design space.

A critical challenge in the data exploration process is discovering and issuing the "right" query, especially when the space of possible queries is large. This problem of exploratory query specification is exacerbated by the use of interactive user interfaces driven by mouse, touch, or next-generation, three-dimensional, motion capture-based devices; which, are often imprecise due to jitter and sensitivity issues. In this paper, we propose SnapToQuery , a novel technique that guides users through the query space by providing interactive feedback during the query specification process by "snapping" to the user's likely intended queries. These intended queries can be derived from prior query logs, or from the data itself, using methods described in this paper. In order to provide interactive response times over large datasets, we propose two data reduction techniques when snapping to these queries. Performance experiments demonstrate that our algorithms help maintain an interactive experience while allowing for accurate guidance. User studies over three kinds of devices (mouse, touch, and motion capture) show that SnapToQuery can help users specify queries quicker and more accurately; resulting in a query specification time speedup of 1.4× for mouse and touch-based devices and 2.2× for motion capture-based devices.

Multitouch interfaces allow users to directly and interactively manipulate data. We propose bringing such interactive manipulation to the task of querying SQL databases. This paper describes an initial implementation of such an interface for multitouch tablet devices called GestureQuery that translates multitouch gestures into database queries. It provides database users with immediate constructive feedback on their queries, allowing rapid iteration and refinement of those queries. Based on preliminary user studies, Gesture-Query is easier to use, and lets users construct target queries quicker than console-based SQL and visual query builders while maintaining interactive performance.

Direct, ad-hoc interaction with databases has typically been performed over console-oriented conversational interfaces using query languages such as SQL. With the rise in popularity of gestural user interfaces and computing devices that use gestures as their exclusive modes of interaction, database query interfaces require a fundamental rethinking to work without keyboards. We present a novel query specification system that allows the user to query databases using a series of gestures. We present a novel gesture recognition system that uses both the interaction and the state of the database to classify gestural input into relational database queries. We conduct exhaustive systems performance tests and user studies to demonstrate that our system is not only performant and capable of interactive latencies, but it is also more usable, faster to use and more intuitive than existing systems.