Crescando
Summary: Demonstrates Crescando, an in-memory distributed relational table delivering predictable latency under volatile workloads via full-table scans, avoiding index contention. Built for high parallelism, it handles many concurrent queries and updates with strict response-time and data freshness guarantees. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,012 | Scalable Pattern Sharing on Event Streams | 2016 | SIGMOD | 6.5338454e-05 |
| 5,541 | A Padded Encoding Scheme to Accelerate Scans by Leveraging Skew | 2015 | SIGMOD | 5.4501856e-05 |
| 5,657 | FluxQuery: An Execution Framework for Highly Interactive Query Workloads | 2016 | SIGMOD | 5.3873634e-05 |
| 6,855 | From Cooperative Scans to Predictive Buffer Management | 2012 | VLDB | 4.9015429e-05 |
| 8,785 | FishStore: Faster Ingestion with Subset Hashing | 2019 | SIGMOD | 4.4467731e-05 |
| 9,303 | Engineering High-Performance Database Engines | 2014 | VLDB | 4.3550373e-05 |
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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 |
|---|---|---|---|---|
| 2,372 | Predictable Performance for Unpredictable Workloads | 2009 | VLDB | 8.940791e-05 |
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