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Vocalizing Large Time Series Efficiently

Summary: Vocalizing Large Time Series Efficiently integrates query evaluation with voice output for time-series results. Optimal experimental design guides sampling of few points to generate concise voice descriptions and select a near-optimal batch. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11642
Venue
VLDB
Year
2018
Pagerank
4.1945683e-05
Overall Rank
11,727 | 18.42%
DOI
10.14778/3236187.3236206

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,007 Data Vocalization with CiceroDB 2019 CIDR 5.2415551e-05
6,908 Demonstrating the Voice-Based Exploration of Large Data Sets with CiceroDB-Zero 2020 VLDB 4.8925595e-05
11,646 A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP 2019 SIGMOD 4.1945683e-05
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