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)
Incoming Non-self Citations Over Time
Authors
- 1. Yanlei Diao (Ecole Polytechnique; INRIA)
- 2. Paweł Guzewicz (Ecole Polytechnique; INRIA)
- 3. Ioana Manolescu (Ecole Polytechnique; INRIA)
- 4. Mirjana Mazuran (Ecole Polytechnique; INRIA)
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)
Showing 1 of 1 citing papers.
| 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)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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