Our goal is to find combinations of facts that optimally summarize data sets. We consider this problem in the context of voice query interfaces for simple, exploratory data analysis. Here, the system answers voice queries with a short summary of relevant data. Finding optimal voice data summaries is computationally expensive. Prior work in this domain has exploited sampling and incremental processing. Instead, we rely on a pre-processing stage generating summaries of data subsets in a batch operation. This step reduces run time overheads by orders of magnitude.We present multiple algorithms for the pre-processing stage, realizing different tradeoffs between optimality and data processing overheads. We analyze our algorithms formally and compare them experimentally with prior methods for generating voice data summaries. We report on multiple user studies with a prototype system implementing our approach. Furthermore, we report on insights gained from a public deployment of our system on the Google Assistant Platform.

Recently proposed voice query interfaces translate voice input into SQL queries. Unreliable speech recognition on top of the intrinsic challenges of text-to-SQL translation makes it hard to reliably interpret user input. We present MUVE (Multiplots for Voice quEries), a system for robust voice querying. MUVE reduces the impact of ambiguous voice queries by filling the screen with multiplots, capturing results of phonetically similar queries. It maps voice input to a probability distribution over query candidates, executes a selected subset of queries, and visualizes their results in a multiplot.

Recently proposed voice query interfaces translate voice input into SQL queries. Unreliable speech recognition on top of the intrinsic challenges of text-to-SQL translation makes it hard to reliably interpret user input. We present MUVE (Multiplots for Voice quEries), a system for robust voice querying. MUVE reduces the impact of ambiguous voice queries by filling the screen with multiplots, capturing results of phonetically similar queries. It maps voice input to a probability distribution over query candidates, executes a selected subset of queries, and visualizes their results in a multiplot. Our goal is to maximize probability to show the correct query result. Also, we want to optimize the visualization (e.g., by coloring a subset of likely results) in order to minimize expected time until users find the correct result. Via a user study, we validate a simple cost model estimating the latter overhead. The resulting optimization problem is NP-hard. We propose an exhaustive algorithm, based on integer programming, as well as a greedy heuristic. As shown in a corresponding user study, MUVE enables users to identify accurate results faster, compared to prior work.