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Promotion Analysis in Multi-Dimensional Space

Summary: PromoRank discovers promotive subspaces where a given object is prominent despite weak global rank. It uses subspace pruning, object pruning, and a promotion cube to prune search space and reduce aggregation cost; experiments on two real datasets show gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10094
Venue
VLDB
Year
2009
Pagerank
7.3501396e-05
Overall Rank
3,521 | 75.85%
DOI
10.14778/1687627.1687641

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wu_vldb09,
        title = {{Promotion Analysis in Multi-Dimensional Space}},
        author = {Wu, Tianyi and Xin, Dong and Mei, Qiaozhu and Han, Jiawei},
        journal = {PVLDB},
        series = {{VLDB} '09},
        doi = {10.14778/1687627.1687641},
        url = {https://doi.org/10.14778/1687627.1687641},
        year = {2009}
}

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