Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

International journal of biomedical imaging·2026
Same author

Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.

Frontiers in neuroinformatics·2026
Same journal

Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific Medical Knowledge.

Big data·2026
Same journal

Explainable Machine Learning-Based Prediction of Postoperative Hypoxemia in Elderly Patients Undergoing General Anesthesia.

Big data·2026
Same journal

Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

Big data·2026
Same journal

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.

Big data·2026
Same journal

Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.

Big data·2026
Same journal

Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems.

Big data·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

一个基于MapReduce的方法,用于从大规模网络中快速连接组件的检测.

Sajid Yousuf Bhat1, Muhammad Abulaish2

  • 1Department of Computer Science, University of Kashmir, Srinagar, Jammu and Kashmir, India.

Big data
|January 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种高效的MapReduce方法,用于在大型网络中找到连接的组件. 该方法通过将组件写入Hadoop分布式文件系统来减少数据传输,从而提高可扩展性和性能.

关键词:
MapReduce和联系人的追踪连接的组件 组件 连接的组件分布式计算分布式计算图表采矿是指采矿的采矿方式.网络分析 网络分析

更多相关视频

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

相关实验视频

Last Updated: Jul 4, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 网络分析 网络分析

背景情况:

  • 由于存储和CPU的局限性,传统的网络处理对于大型的现实世界网络是不可行的.
  • 现实世界的网络数据通常是分布式的,需要像MapReduce这样的分布式处理框架.
  • 连接组件检测的现有MapReduce方法在最小化循环和数据传输方面面临挑战.

研究的目的:

  • 提出一种高效的基于MapReduce的方法,用于在大规模网络中检测连接组件.
  • 为了减少MapReduce轮次的数量和后续阶段处理的数据量.
  • 证明拟议方法在接触追踪中的应用.

主要方法:

  • 开发了一种高效的MapReduce算法,用于连接组件的检测.
  • 实施了一种策略,在发现时将连接的组件写入Hadoop分布式文件系统 (HDFS).
  • 通过利用HDFS存储,减少了转发到后续MapReduce轮的数据.

主要成果:

  • 与现有方法相比,拟议的方法大大减少了数据转发.
  • 经验评估表明,在大型网络数据集上,性能和可扩展性优越.
  • 该方法在接触追踪等应用中被证明是有效的.

结论:

  • 新的MapReduce方法为大型网络中连接组件的检测提供了一个高效和可扩展的解决方案.
  • 通过最小化数据传输,该方法克服了传统分布式处理技术的局限性.
  • 这种方法非常适合需要大规模网络分析的现实应用.