检索结果-内蒙古大学图书馆

26th Pan-Hellenic Conference on Informatics, PCI 2022

作者： Kalogeras, Georgios Tsakanikas, Vassilios Ballas, Ioannis Aggelopoulos, Vassilios Tampakas, Vassilios Department of Electrical and Computer Engineering Distributed Intelligent Systems and Data Lab University of Peloponnese Greece

ISBN: (纸本)9781450398541

The proliferation of data generation devices, including IoT and edge computing has led to the big data paradigm, which has considerably placed pressure on well-established relational databases during the last decade. Researchers have proposed several alternative database models in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. More specifically, the two frameworks are compared on their capacity to execute community detection algorithms. © 2022 ACM.

关键词： Graph databases

来源：评论

学校读者我要写书评

暂无评论

Community Detection at scale: A comparison study among Apache Spark and Neo4j 22

Community Detection at scale: A comparison study among Apach...

引用

Proceedings of the 26th Pan-Hellenic Conference on Informatics

作者： Georgios Kalogeras Vassilios Tsakanikas Ioannis Ballas Vassilios Aggelopoulos Vassilios Tampakas Department of Electrical and Computer Engineering Distributed Intelligent Systems and Data Lab University of Peloponnese Greece

ISBN: (纸本)9781450398541

关键词： Neo4j graph database Apache Spark label Propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Assessing the computational limits of GraphDBs' engines - A comparison study between Neo4j and Apache Spark 24

Assessing the computational limits of GraphDBs' engines - A ...

引用

24th Pan-Hellenic Conference on Informatics, PCI 2020

作者： Ballas, Ioannis Tsakanikas, Vassilios Pefanis, Evaggelos Tampakas, Vassilios Distributed Intelligent Systems and Data Lab Department of Electrical and Computer Engineering University of Peloponnese Greece

ISBN: (纸本)9781450388979

Big data paradigm has placed pressure on well-established relational databases during the last decade. Both academia and industry have proposed several alternative database schemes in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. The results reveal that Neo4j is limited to the physical RAM memory of the processing environment. Yet, until this limit is reached, the processing engine of Neo4j outperforms the Apache Spark engine. © 2020 ACM.

关键词： Graph databases

来源：评论

学校读者我要写书评

暂无评论

Younger: The First dataset for Artificial Intelligence-Generated Neural Network Architecture

arXiv

引用

arXiv 2024年

作者： Yang, Zhengxin Gao, Wanling Peng, Luzhou Huang, Yunyou Tang, Fei Zhan, Jianfeng Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Inspur Group Co. Ltd. China Jinan Inspur Data Technology Co. Ltd. China

Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing the complexity of automatically generating neural network architecture from scratch, we introduce Younger, a pioneering dataset to advance this ambitious goal. Derived from over 174K real-world models across more than 30 tasks from various public model hubs, Younger includes 7,629 unique architectures, and each is represented as a directed acyclic graph with detailed operator-level information. The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement. By establishing these capabilities, Younger contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments explore the potential and effectiveness of Younger for automated architecture generation and, as a secondary benefit, demonstrate that Younger can serve as a benchmark dataset, advancing the development of graph neural networks. We release the dataset2 and code3 publicly to lower the entry barriers and encourage further research in this challenging area. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

引用

SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：