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Attraction and Avoidance Detection from Movements

Summary: Unified statistical framework for detecting attraction, avoidance, and neutrality between moving objects via permutation tests against movement-background models. Pruning strategies make significance estimation efficient, while threshold queries retrieve strongly related objects at scale. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11010
Venue
VLDB
Year
2014
Pagerank
5.093636e-05
Overall Rank
12,188 | 16.38%
DOI
10.14778/2732232.2732235

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@article{li_vldb14,
        title = {{Attraction and Avoidance Detection from Movements}},
        author = {Li, Zhenhui and Ding, Bolin and Wu, Fei and Lei, Tobias Kin Hou and Kays, Roland and Crofoot, Margaret C.},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {3},
        pages = {157},
        doi = {10.14778/2732232.2732235},
        url = {https://doi.org/10.14778/2732232.2732235},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
12,190 MoveMine 2.0: Mining Object Relationships from Movement Data 2014 VLDB 5.093636e-05
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