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Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives

Summary: Introduces a general method for sliding-window streaming maximization that bypasses exponential/smooth-histogram limits to achieve sublinear space and update time. Instantiated for submodular, diversity and subadditive objectives with cardinality constraints, improving prior problem-specific bounds. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1795
Venue
PODS
Year
2019
Pagerank
5.5422948e-05
Overall Rank
7,797 | 46.51%
DOI
10.1145/3294052.3319701

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{borassi_pods19,
        address = {New York, NY, USA},
        series = {{PODS} '19},
        title = {{Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives}},
        url = {https://dl.acm.org/doi/10.1145/3294052.3319701},
        doi = {10.1145/3294052.3319701},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Borassi, Michele and Epasto, Alessandro and Lattanzi, Silvio and Vassilvitskii, Sergei and Zadimoghaddam, Morteza},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,691 On Reporting Durable Patterns in Temporal Proximity Graphs 2024 PODS 5.3839057e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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