EDBT 2026 Demo / reviewers in the wild / expert
Zhenge Chen
dblp:338/0000
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Transaction processing and concurrency control · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Transaction processing and concurrency control › isolation levels
snapshot isolation |
0.7 | 1 | 2023 | Efficient Black-box Checking of Snapshot Isolation in Databases · Proc. VLDB Endow. 2023 |
Transaction processing and concurrency control
isolation levels |
0.2 | 1 | 2023 | Efficient Black-box Checking of Snapshot Isolation in Databases · Proc. VLDB Endow. 2023 |
Methods — techniques the papers use, named apart from their topics
generalized polygraph · 0.7constraint encoding · 0.7SMT solver · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Efficient Black-box Checking of Snapshot Isolation in DatabasesabstractSnapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing a compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As our extensive assessment demonstrates, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads. Kaile Huang, Si Liu 0003, Zhenge Chen, Hengfeng Wei, David A. Basin, Haixiang Li, Anqun Pan |
Proc. VLDB Endow. | 3 |