EDBT 2026 Demo / reviewers in the wild / expert
Chengyou Shen
dblp:414/4807
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 67% Memory systems · 33% | |
| Databases, data mining, and information retrieval
1 paper |
Indexing and storage engines · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines › spatial index
r-tree |
0.9 | 1 | 2025 | Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025 |
Indexing and storage engines
spatial index |
0.9 | 1 | 2025 | Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025 |
Storage systems
crash consistency |
0.9 | 1 | 2025 | Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025 |
Memory systems
non-volatile memory |
0.9 | 1 | 2025 | Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025 |
Storage systems
storage reliability |
0.9 | 1 | 2025 | Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025 |
Methods — techniques the papers use, named apart from their topics
persistence operations · 1.7hilbert curve · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Hybrid DRAM-NVM R-Trees with Consistency GuaranteeabstractThe non-volatile memory (NVM) with DRAM-like performance and disk-like persistency has attracted considerable attention in a variety of index structures, including hash table, B-Tree and R-Tree. However, existing NVM-optimized consistent R-Tree is still suboptimal because its single level system neglects the potential boost that DRAM can bring. In this paper, we first propose a hybrid DRAM-NVM consistent R-Tree (HR-Tree), which separately stores internal nodes in DRAM and leaf nodes in NVM. To avoid inconsistency, HR-Tree uses several auxiliary flag bits and pointers to record the process of writes to NVM and employs persistence operations to strictly control the order of writes to NVM. To reduce DRAM consumption, which mainly depends on the metadata size of a leaf node, we present a shared byte strategy to abolish restrictions on metadata size while still keeping HR-Tree consistency. Next, for further shortening search time, we propose an alternative Hilbert-curve-based hybrid R-Tree (HHR-Tree). It has better search efficiency yet leads to insertion performance degradation. Contrary to in-place update in HR-Tree, HHR-Tree applies out-of-place mechanism to enforce data consistency. We conduct comprehensive evaluations on Intel Optane DC Persistent Memory. The proposed HR-Tree outperforms FBR-Tree in terms of insertion, deletion and search throughput while HHR-Tree exhibits a significant improvement for search performance by sacrificing insertion efficiency. Chengyou Shen, Shengfei Shi, Hong Gao 0001, Yaofeng Tu |
ICDE | 2 |