Demo Data from EDBT 2026 / reviewers in the wild / expert
Yicong Zhu
dblp:295/1506
· DBLP profile ↗
5ranked-venue papers
0as first author
5since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 since 2021Theory of computation · 2 · 2 since 2021Security and privacy · 1 · 1 since 2021Databases, 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 77% Cloud and datacenter computing · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Indexing and storage engines · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines › storage management
storage engine |
1.0 | 1 | 2026 | PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases · FAST 2026 |
Storage systems
data compression |
1.0 | 1 | 2026 | PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases · FAST 2026 |
Cloud and datacenter computing › cloud data management
cloud-native database |
0.3 | 1 | 2026 | PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases · FAST 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | PolarStore: High-Performance Data Compression for Large-Scale Cloud-Native Databases
Qingda Hu, Xinjun Yang, Feifei Li 0001, Ya Lin, Yicong Zhu, Rongbiao Xie, Bin Wu 0003, Wenchao Zhou |
FAST | 7 |
| 2026 | Parameterized algorithms and complexity for scheduling with precedence constraints and time windows
Feng Shi 0003, Na Feng, Yicong Zhu, Guangwei Wu |
Theor. Comput. Sci. | 3 |
| 2025 | Improved Parameterized Algorithms for Scheduling with Precedence Constraints and Time Windows
Feng Shi 0003, Yicong Zhu, Guangwei Wu, Jianxin Wang 0001 |
COCOON (2) | 2 |
| 2022 | Compositional Model Checking of Consensus Protocols via Interaction-Preserving AbstractionabstractConsensus protocols are widely used in building reliable distributed software systems and their correctness is of vital importance. TLA+ is a lightweight formal specification language which enables precise specification of system design and exhaustive checking of the design without any human effort. The features of TLA+ make it widely used in the specification and model checking of consensus protocols, both in academia and in industry. However, the application of TLA+ is limited by the state explosion problem in model checking. Though compositional model checking is essential to tame the state explosion problem, existing compositional checking techniques do not sufficiently consider the characteristics of TLA+. In this work, we propose the Interaction-Preserving Abstraction (IPA) framework, which leverages the features of TLA+ and enables practical and efficient compositional model checking of consensus protocols specified in TLA+. In the IPA framework, system specification is partitioned into multiple modules, and each module is divided into the internal part and the interaction part. The basic idea of the interaction-preserving abstraction is to omit the internal part of each module, such that another module cannot distinguish whether it is interacting with the original module or the coarsened abstract one. We apply the IPA framework to the compositional checking of the TLA+ specifications of two consensus protocols Raft and ParallelRaft. Raft is a consensus protocol which was originally developed in academia and then widely used in industry. ParallelRaft is the replication protocol in PolarFS, the distributed file system for the commercial database Alibaba PolarDB. We demonstrate that the IPA framework is easy to use in realistic scenarios and at the same time significantly reduces the model checking cost. Xiaosong Gu, Yicong Zhu, Yu Huang 0002, Xiaoxing Ma |
SRDS | 3 |
| 2022 | Scalable multiscale modeling of platelets with 100 million particles
Changnian Han, Yicong Zhu, Guojing Cong, James R. Kozloski, Chih-Chieh Yang, Leili Zhang, Yuefan Deng |
J. Supercomput. | 3 |