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Efficient Exploration of Interesting Aggregates in RDF Graphs

Summary: End-to-end framework to automatically identify top-k interesting aggregates on RDF graphs, discovering facts, dimensions, and measures within a lattice. RDF-friendly one-pass lattice evaluation with probabilistic early-stop pruning; up to 2.9x speedup and scalable performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6259
Venue
SIGMOD
Year
2021
Pagerank
5.2869213e-05
Overall Rank
9,339 | 35.93%
DOI
10.1145/3448016.3457307

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{diao_sigmod21,
        title = {{Efficient Exploration of Interesting Aggregates in RDF Graphs}},
        author = {Diao, Yanlei and Guzewicz, Paweł and Manolescu, Ioana and Mazuran, Mirjana},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457307},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457307},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
8,520 Knowledge Graph Exploration Systems: are we lost? 2022 CIDR 5.4119882e-05
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Outgoing Citations (Sorted by Pagerank)

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