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

Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K
Rapidly Varying Flow01:24

Rapidly Varying Flow

146
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
146

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

Management of type 2 diabetes mellitus using Yoga: Implications for immunological and autonomic dysfunction.

Bioinformation·2026
Same author

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake.

MethodsX·2023
Same author

Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

PloS one·2022
Same author

Comparative evaluation of the compressive strength of two different post systems in primary anterior teeth restored with pediatric zirconia crowns.

Journal of the Indian Society of Pedodontics and Preventive Dentistry·2020
Same author

Indigenous tooth powders = covert lead poisoning?

Indian journal of dental research : official publication of Indian Society for Dental Research·2014
Same author

A rare presentation of pulmonary lymphangitic carcinomatosis in cancer of lip: case report.

World journal of surgical oncology·2011

相关实验视频

Updated: Sep 19, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K

基于滑动窗口的罕见部分周期性模式挖掘算法,在时间数据流上进行挖掘.

K Jyothi Upadhya1, Ronan Lobo1, Mini Shail Chhabra1

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.

Frontiers in big data
|June 19, 2025
PubMed
概括

这项研究引入了两种新方法,R3PStreamSW-Growth和R3PStreamSW-BitVectorMiner,用于在时间数据流中找到罕见的部分周期性模式. 在各种数据集的速度和效率上,R3PStreamSW-BitVectorMiner显著超过R3PStreamSW-Growth.

关键词:
基于列表的采矿流采矿.部分周期性开采采矿.罕见的部分周期性模式采矿.罕见的周期性模式采矿采矿流是周期性模式的采矿.基于树木的溪流采矿采矿.

更多相关视频

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.3K

相关实验视频

Last Updated: Sep 19, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.3K

科学领域:

  • 数据挖掘 数据挖掘
  • 模式识别 模式识别
  • 大数据分析大数据分析

背景情况:

  • 定期模式挖掘对于了解各个行业的大数据集至关重要.
  • 现有的方法很难从时间数据流中提取罕见的部分周期性模式.
  • 时间和流数据集需要专门的算法来分析模式的发生.

研究的目的:

  • 提出新的算法,从时间数据流中提取罕见的部分周期性模式.
  • 为了解决当前处理数据流中的时间信息的方法的局限性.
  • 为实时模式挖矿开发高效的单扫描方法.

主要方法:

  • 推出了两个基于滑动窗口的单扫描算法:R3PStreamSW-Growth和R3PStreamSW-BitVectorMiner.
  • 专注于在时间数据流中挖掘罕见的部分周期性模式.
  • 在密集和稀疏数据集上评估算法性能,包括事故和T10I4D100K.

主要成果:

  • 在R3PStreamSW-BitVectorMiner上,R3PStreamSW-Growth表现出了比R3PStreamSW更高的性能.
  • 在密集的事故数据集上观察到大约93%的性能增长.
  • 在R3PStreamSW-BitVectorMiner的稀疏T10I4D100K数据集上发现了90%的性能提升.

结论:

  • R3PStreamSW-BitVectorMiner比R3PStreamSW-Growth快得多,而且效率也更高.
  • 提出的方法有效地从时间数据流中提取罕见的部分周期性模式.
  • 这些发现突显了R3PStreamSW-BitVectorMiner在数据流分析中的真实应用中的潜力.