QUEPA: QUerying and Exploring a Polystore by Augmentation

Summary: QUEPA introduces augmented search and exploration for polystores, automatically enriching cross-store results without a global schema. Plug-and-play, middleware-free integration preserving native languages for lightweight cross-engine data sharing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5227
Venue: SIGMOD
Year: 2016
Pagerank: 5.3114559e-05
Overall Rank: 9,158 | 37.17%
DOI: 10.1145/2882903.2899393

Incoming Non-self Citations Over Time

Authors

1. Antonio Maccioni (Roma Tre University)
2. Edoardo Basili (Roma Tre University)
3. Riccardo Torlone (Roma Tre University)

BibTeX Citation

@inproceedings{maccioni_sigmod16,
        title = {{QUEPA: QUerying and Exploring a Polystore by Augmentation}},
        author = {Maccioni, Antonio and Basili, Edoardo and Torlone, Riccardo},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2899393},
        url = {https://dl.acm.org/doi/10.1145/2882903.2899393},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,741	Polyglot Data Management: State of the Art & Open Challenges	2022	VLDB	5.227679e-05

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
3,350	Support the Data Enthusiast: Challenges for Next-Generation Data-Analysis Systems	2014	VLDB	7.4942163e-05

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