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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)

Paper ID
h843609ad4cc65caf
Venue
VLDB
Year
2017
Pagerank
4.9793485e-05
Overall Rank
12,300 | 17.31%
DOI
10.14778/3137628.3137663

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Authors

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}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
29 Fast Algorithms for Mining Association Rules 1994 VLDB 0.0005121339
1,831 λόγος: A System for Translating Queries into Narratives 2012 SIGMOD 9.5460598e-05
1,877 Making the Case for Query-by-Voice with EchoQuery 2016 SIGMOD 9.4533419e-05
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