Chengyou Shen

dblp:414/4807 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Indexing and storage engines › spatial index
r-tree
0.912025
Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025
Indexing and storage engines
spatial index
0.912025
Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025
Storage systems
crash consistency
0.912025
Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025
Memory systems
non-volatile memory
0.912025
Hybrid DRAM-NVM R-Trees with Consistency Guarantee · ICDE 2025
Storage systems
storage reliability
0.912025
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
YearPublicationVenuePosition
2025 Hybrid DRAM-NVM R-Trees with Consistency Guarantee
abstract
The 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
ICDE2