Demo Data from EDBT 2026 / reviewers in the wild / expert
Chengxin Xie
dblp:303/7642
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
0000-0003-0814-4773ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › storage reliability
erasure coding |
0.5 | 1 | 2021 | Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021 |
Storage systems
repair |
0.5 | 1 | 2021 | Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021 |
Storage systems
storage reliability |
0.5 | 1 | 2021 | Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021 |
Storage systems › distributed storage
storage cluster |
0.1 | 1 | 2021 | Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis · IEEE Trans. Computers 2021 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.5cloud experimentation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Bidirectional semantic accumulation for deep graph representation learning via autoencoders
Chengxin Xie, Qiya Song, Jingui Huang |
Pattern Recognit. | 1 |
| 2021 | Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and AnalysisabstractErasure coding is a storage-efficient means to guarantee data reliability in today's commodity storage systems, yet its repair performance is seriously hindered by the substantial repair traffic. Repair in clustered storage systems is even complicated because of the scarcity of the cross-cluster bandwidth. We present${\sf ClusterSR}$, a cluster-aware scattered repair approach.${\sf ClusterSR}$minimizes the cross-cluster repair traffic by carefully choosing the clusters for reading and repairing chunks. It further balances the cross-cluster repair traffic by scheduling the repair of multiple chunks. Large-scale simulation and Alibaba Cloud ECS experiments show that${\sf ClusterSR}$can reduce 5.6-52.7 percent of the cross-cluster repair traffic and improve 14.4–68.8 percent of the repair throughput. Zhirong Shen, Shiyao Lin, Jiwu Shu, Chengxin Xie, Yingxun Fu |
IEEE Trans. Computers | 4 |