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Extracting Top-K Insights from Multi-dimensional Data

Summary: Introduces automatic top-k insights from multi-dimensional data via a new 'insight' concept rooted in multi-step aggregations. Offers a scoring function and pruning/ordering/cube-sharing framework to compute top-k insights efficiently. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5350
Venue
SIGMOD
Year
2017
Pagerank
7.7246394e-05
Overall Rank
3,132 | 78.52%
DOI
10.1145/3035918.3035922

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tang_sigmod17,
        title = {{Extracting Top-K Insights from Multi-dimensional Data}},
        author = {Tang, Bo and Han, Shi and Yiu, Man Lung and Ding, Rui and Zhang, Dongmei},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3035922},
        url = {https://dl.acm.org/doi/10.1145/3035918.3035922},
        year = {2017}
}

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