Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data
Summary: Benchmark for real-time interactive querying on large data, grounded in real user traces from crossfilter-style visualization workloads. Open benchmark artifacts—datasets, interaction sequences, SQL queries, and analysis code—to advance reproducible research in interactive data exploration. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Leilani Battle (University of Maryland)
- 2. Philipp Eichmann (Brown University)
- 3. Marco Angelini (Sapienza University)
- 4. Tiziana Catarci (Sapienza University)
- 5. Giuseppe Santucci (Sapienza University)
- 6. Yukun Zheng (University of Maryland)
- 7. Carsten Binnig (Technical University of Darmstadt)
- 8. Jean-Daniel Fekete (Centre National de la Recherche Scientifique; INRIA; University of Paris)
- 9. Dominik Moritz (University of Washington)
BibTeX Citation
@inproceedings{battle_sigmod20,
title = {{Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data}},
author = {Battle, Leilani and Eichmann, Philipp and Angelini, Marco and Catarci, Tiziana and Santucci, Giuseppe and Zheng, Yukun and Binnig, Carsten and Fekete, Jean-Daniel and Moritz, Dominik},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389732},
url = {https://dl.acm.org/doi/10.1145/3318464.3389732},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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