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)
Incoming Non-self Citations Over Time
Authors
- 1. Bo Tang (Hong Kong Polytechnic University)
- 2. Shi Han (Microsoft)
- 3. Man Lung Yiu (Hong Kong Polytechnic University)
- 4. Rui Ding (Microsoft)
- 5. Dongmei Zhang (Microsoft)
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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