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Gang Xiao 0003

dblp:11/2966-3 · DBLP profile ↗
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8ranked-venue papers
0as first author
7since 2021 · last 2024
—ORCID · conflict

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

Artificial intelligence and machine learning · 4 · 4 since 2021Systems, architecture and hardware · 2 · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1

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.

Network and information security
1 paper
Blockchain and cryptocurrency security · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Databases, data mining, and information retrieval
1 paper
Transaction processing and concurrency control · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Transaction processing and concurrency control
distributed commit protocols
0.812024
Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding · IEEE Trans. Computers 2024
Blockchain and cryptocurrency security › blockchain scalability
blockchain sharding
0.812024
Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding · IEEE Trans. Computers 2024
Blockchain and cryptocurrency security › blockchain scalability › blockchain sharding
cross-shard transaction
0.812024
Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding · IEEE Trans. Computers 2024
Distributed systems
consensus
0.812024
Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding · IEEE Trans. Computers 2024
Distributed systems › consensus › scalable consensus
cross-shard consensus
0.812024
Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding · IEEE Trans. Computers 2024

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

off-chain execution · 2.3calling-flow analysis · 2.3
YearPublicationVenuePosition
2024 Efficient Execution of Arbitrarily Complex Cross-Shard Contracts for Blockchain Sharding
abstract
Sharding is a promising solution to enhance the scalability of blockchain. However, previous sharding systems adopt the lock-based cross-shard protocol to exclusively handle one-shot cross-shard transactions, leading to low-efficiency executions and unavailable calls when handling complex cross-shard contracts that introduce multi-shot cross-shard transactions to invoke multiple contracts managed by different shards.In this paper, we aim to enable efficient execution of arbitrarily complex cross-shard contracts in blockchain sharding systems. First, we perform a calling-flow analysis on Ethereum contracts with more than 180 million real-world transactions and find that about 30% transactions invoke complex contracts. Then, motivated by the properties of these complex contracts, we propose an off-chain execution model, called ShardCon, to achieve efficient executions for complex cross-shard contracts by decoupling the contract execution from the cross-shard consensus. Next, we introduce a cross-shard contract execution engine and a contract-driven deployment rule to the overheads introduced by off-chain executions. Moreover, to adapt to the multi-chain property of a sharding system, we introduce an off-chain state atomic commit protocol. Finally, we implement a prototype and evaluate it with concrete cross-shard contracts, showing that ShardCon can achieve more than 10x increase in throughput and 2x decrease in confirmation latency than the state-of-the-art sharding systems.
Wuhui Chen, Zicong Hong, Gang Xiao 0003, Linlin Du, Zibin Zheng
IEEE Trans. Computers4
2022 A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer
Ruxin Zhao, Yongli Wang 0002, Gang Xiao 0003, Hao Li 0050
Appl. Intell.3
2021 Batch mode active learning via adaptive criteria weights
Hao Li 0050, Yongli Wang 0002, Yanchao Li 0001, Gang Xiao 0003, Ruxin Zhao
Appl. Intell.4
2021 Correction to: Batch mode active learning via adaptive criteria weights
Hao Li 0050, Yongli Wang 0002, Yanchao Li 0001, Gang Xiao 0003, Ruxin Zhao
Appl. Intell.4
2021 Learning adaptive criteria weights for active semi-supervised learning
Hao Li 0050, Yongli Wang 0002, Yanchao Li 0001, Gang Xiao 0003, Ruxin Zhao
Inf. Sci.4
2021 Recommendation system based on semantic scholar mining and topic modeling on conference publications
Hamed Jelodar, Yongli Wang 0002, Gang Xiao 0003, Mahdi Rabbani, Ruxin Zhao, Seyedvalyallah Ayobi, Isma Masood
Soft Comput.3
2021 A selfish herd optimization algorithm based on the simplex method for clustering analysis
Ruxin Zhao, Yongli Wang 0002, Gang Xiao 0003, Hao Li 0050
J. Supercomput.3
2020 An efficient privacy-preserving data query and dissemination scheme in vehicular cloud
Yongli Wang 0002, Gang Xiao 0003, Junlong Zhou, Bei Gong
Pervasive Mob. Comput.3