Vocalizing large time series efficiently
Immanuel Trummer, Mark Bryan, Ramya Narasimha · Proceedings of the VLDB Endowment · 2018
We vocalize query results for time series data. We describe a holistic approach that integrates query evaluation and vocalization. In particular, we generate only those parts of the query result that are relevant for voice output. We exploit the fact that voice output has to be concise and simple to be understandable for listeners. Hence, the problem of generating voice output reduces to choosing between several coarse-grained alternatives. To make that choice, it is sufficient to evaluate the time series at a few carefully chosen locations. We use techniques from the area of optimal experimental design to choose optimal sampling points. Our algorithm is iterative and generates in each iteration a set of promising voice description candidates. We consider multiple metrics when generating voice descriptions, including the accuracy of description as well as its complexity and length. Then, we choose a near-optimal batch of sampling points to refine our choice between promising candidates. We compare this algorithm experimentally against several baselines, demonstrating superior performance in terms of execution time and output quality. We also conducted a user study, showing that it enables users to execute simple exploratory data analysis via voice descriptions alone. We also compare against visual interfaces and sonification (i.e., non-speech sound) interfaces in terms of user performance.