DBScholar

Back to papers

A Stochastic Approach for Clustering in Object Bases

Summary: Optimal object clustering under probabilistic access patterns is rigorously formulated for object bases. In a second model, clustering is NP-complete and reduces to graph partitioning; a Kernighan-style heuristic is proposed with experiments against baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2571
Venue
SIGMOD
Year
1991
Pagerank
9.9585965e-05
Overall Rank
1,709 | 88.28%
DOI
10.1145/115790.115792

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tsangaris_sigmod91,
        title = {{A Stochastic Approach for Clustering in Object Bases}},
        author = {Tsangaris, Manolis M. and Naughton, Jeffrey F.},
        series = {{SIGMOD} '91},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/115790.115792},
        url = {https://dl.acm.org/doi/10.1145/115790.115792},
        year = {1991}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
2,229 The Performance and Utility of the Cactis Implementation Algorithms 1990 VLDB 8.9030801e-05
Previous Page 1 / 1 Next

Semantically Similar Papers