Data Vocalization: Optimizing Voice Output of Relational Data
Summary: Formulates data vocalization as minimizing speech time for approximate relational-table transmission under precision and cognitive-load constraints, proving NP-hardness. Proposes ILP, Apriori-style row grouping, and a polynomial-time submodular greedy method with near-optimality guarantees. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Immanuel Trummer (Cornell University)
- 2. Jiancheng Zhu (Cornell University)
- 3. Mark Bryan (Cornell University)
BibTeX Citation
@article{trummer_vldb17,
title = {{Data Vocalization: Optimizing Voice Output of Relational Data}},
author = {Trummer, Immanuel and Zhu, Jiancheng and Bryan, Mark},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {11},
pages = {1574--1587},
doi = {10.14778/3137628.3137663},
url = {https://doi.org/10.14778/3137628.3137663},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,964 | Data Vocalization with CiceroDB | 2019 | CIDR | 5.7303405e-05 |
| 7,511 | Demonstrating the Voice-Based Exploration of Large Data Sets with CiceroDB-Zero | 2020 | VLDB | 5.6029996e-05 |
| 11,582 | BABOONS: Black-Box Optimization of Data Summaries in Natural Language | 2022 | VLDB | 5.093636e-05 |
| 11,841 | A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP | 2019 | SIGMOD | 5.093636e-05 |
| 11,932 | Vocalizing Large Time Series Efficiently | 2018 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 1,808 | λόγος: A System for Translating Queries into Narratives | 2012 | SIGMOD | 9.7050949e-05 |
| 1,828 | Making the Case for Query-by-Voice with EchoQuery | 2016 | SIGMOD | 9.6689075e-05 |
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