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Hexiang Pan

dblp:339/8942 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0004-6350-4887ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 3 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
2 papers
Distributed systems · 70% Cloud and datacenter computing · 15% Storage systems · 15%
Databases, data mining, and information retrieval
1 paper
Transaction processing and concurrency control · 100%
Network and information security
1 paper
Hardware security and side channels · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › distributed database
commit protocol
0.812024
FC: Adaptive Atomic Commit via Failure Detection · ICDE 2024
Distributed systems › distributed database
distributed transactions
0.812024
FC: Adaptive Atomic Commit via Failure Detection · ICDE 2024
Distributed systems › fault tolerance
failure detection
0.812024
FC: Adaptive Atomic Commit via Failure Detection · ICDE 2024
Distributed systems
fault tolerance
0.812024
FC: Adaptive Atomic Commit via Failure Detection · ICDE 2024
Cloud and datacenter computing › cloud data management
cloud-native database
0.712023
VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation · Proc. ACM Manag. Data 2023
Storage systems › distributed storage
disaggregated storage
0.712023
VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation · Proc. ACM Manag. Data 2023
Hardware security and side channels › trusted execution environments
Intel SGX
0.212023
VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation · Proc. ACM Manag. Data 2023
Hardware security and side channels
trusted execution environments
0.212023
VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation · Proc. ACM Manag. Data 2023

Methods — techniques the papers use, named apart from their topics

trusted hardware · 2.0page-structure cache · 2.0reinforcement learning · 0.8
YearPublicationVenuePosition
2025 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
Zhanhao Zhao, Shaofeng Cai, Hexiang Pan, Siqi Xiang, Naili Xing, Gang Chen 0001, Beng Chin Ooi, Yanyan Shen, Yuncheng Wu, Meihui Zhang 0001
CIDR4
2024 FC: Adaptive Atomic Commit via Failure Detection
abstract
Atomic commit protocols (ACPs) are crucial for ensuring transaction atomicity in distributed transaction processing. However, existing ACPs, designed specifically for fixed failure conditions, cannot work efficiently in modern environments, where failures such as node crashes and connection delays can happen anytime due to the use of commodity nodes and networks. In this paper, we propose FC, a novel and practical ACP that can adapt to changes in failure conditions. In essence, FC includes three dedicated protocols, which are specifically designed for three different failure conditions: (i) failure-free: no failure occurs, (ii) crash-failure: nodes might crash but there is no delayed connection, or (iii) network-failure: both crashed nodes and delayed connection can occur. During its operation, FC can monitor if any failure occurs and dynamically switch to the most suitable protocol, using a protocol selector, whose parameters are fine-tuned by reinforcement learning. Consequently, FC improves transaction performance and robustly ensures fault tolerance when crash failures and network failures occur. We conduct extensive experiments to evaluate FC with both YCSB and TPC-C benchmarks. The experimental results show that FC achieves up to 2.88x higher throughput and 3.76x lower latency than state-of-the-art ACPs, and its sustainable performance when integrated with two popular databases, namely MongoDB and PostgreSQL.
Hexiang Pan, Quang-Trung Ta, Meihui Zhang 0001, Zhanhao Zhao, Yeow Meng Chee, Gang Chen 0001, Beng Chin Ooi
ICDE1
2023 VeriTxn: Verifiable Transactions for Cloud-Native Databases with Storage Disaggregation
abstract
Cloud-native databases become increasingly popular while exposing to greater data security and correctness risks. Existing verifiable outsourced databases overlook either the correctness risk of transactions, or the disaggregation architecture: a key design consideration of cloud-native databases for performance and elasticity, or both. We present VeriTxn, a novel cloud-native database that efficiently provides verifiability of transaction correctness. VeriTxn relies on the trusted hardware (i.e., Intel SGX) to enable verifiable transaction processing. We build a page-structure cache in the trusted domain, where transactions can be verified with low, constant overhead. VeriTxn further optimizes the read-only transactions by exploiting disaggregation to fit the read-heavy workload in the cloud. We also integrate our proposal into MySQL, a popular open-source database. We conduct extensive experiments to compare VeriTxn against state-of-the-art verifiable databases and evaluate the performance of VeriTxn on MySQL. The results show that VeriTxn introduces tolerable performance degradation for verifiable transactions, while achieving up to 7.03× and 7.93× higher throughput than Litmus and LedgerDB, and its sustainable performance when integrated with MySQL.
Zhanhao Zhao, Hexiang Pan, Gang Chen 0001, Xiaoyong Du 0001, Wei Lu 0015, Beng Chin Ooi
Proc. ACM Manag. Data2