{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:15:56Z","timestamp":1758266156076},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>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.<\/jats:p>\n          <jats:p>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.<\/jats:p>\n          <jats:p>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.<\/jats:p>","DOI":"10.14778\/3236187.3236206","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1563-1575","source":"Crossref","is-referenced-by-count":4,"title":["Vocalizing large time series efficiently"],"prefix":"10.14778","volume":"11","author":[{"given":"Immanuel","family":"Trummer","sequence":"first","affiliation":[{"name":"Cornell University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Bryan","sequence":"additional","affiliation":[{"name":"Cornell University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramya","family":"Narasimha","sequence":"additional","affiliation":[{"name":"Cornell University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon. 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