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Latent OLAP: Data Cubes over Latent Variables

Summary: Latent-variable cubes; aggregates over unobserved factors via Bayesian hierarchical models for OLAP. Defines the framework rigorously, addresses pitfalls of ignoring latent vars, and delivers efficient algorithms with experiments showing accuracy and speedups over baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4501
Venue
SIGMOD
Year
2011
Pagerank
5.093636e-05
Overall Rank
12,370 | 15.14%
DOI
10.1145/1989323.1989415

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BibTeX Citation

@inproceedings{agarwal_sigmod11,
        title = {{Latent OLAP: Data Cubes over Latent Variables}},
        author = {Agarwal, Deepak and Chen, Bee-Chung},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989415},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989415},
        year = {2011}
}

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