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Cohort Query Processing

Summary: Extends SQL with three new operators to support cohort analysis on large user activity tables. Three evaluation schemes are explored: two non-intrusive and a columnar variant with cohort-specific optimizations; experiments show the columnar approach outperforms the others. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h1765c33d295e36dc
Venue
VLDB
Year
2017
Pagerank
5.3542169e-05
Overall Rank
8,324 | 44.04%
DOI
10.14778/3015270.3015271

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{jiang_vldb17,
        title = {{Cohort Query Processing}},
        author = {Jiang, Dawei and Cai, Qingchao and Chen, Gang and Jagadish, H. V. and Ooi, Beng Chin and Tan, Kian-Lee and Tung, Anthony K. H.},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {1},
        doi = {10.14778/3015270.3015271},
        url = {https://doi.org/10.14778/3015270.3015271},
        year = {2017}
}

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6,233 Fine-grained Concept Linking using Neural Networks in Healthcare 2018 SIGMOD 5.8420918e-05
8,271 Sigma Workbook: A Spreadsheet for Cloud Data Warehouses 2022 VLDB 5.3643259e-05
11,577 CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics 2024 VLDB 4.9793485e-05
12,148 Data Pipelines for User Group Analytics 2019 SIGMOD 4.9793485e-05
12,257 Effective Temporal Dependence Discovery in Time Series Data 2018 VLDB 4.9793485e-05
13,832 Cohort Analysis with Ease 2018 SIGMOD -
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

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