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

Impact of Schemas01:30

Impact of Schemas

185
Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
185
Schemas01:42

Schemas

12.3K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
12.3K
Nursing Clinical Information System01:27

Nursing Clinical Information System

1.2K
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
1.2K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Data Collection by Observations01:08

Data Collection by Observations

14.4K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
14.4K

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 journal

CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.

Journal of big data·2026
Same journal

Comprehensive representation of health-related phenotypes in one million dogs using topic modelling of electronic health records.

Journal of big data·2026
Same journal

<i>F</i>u<i>n</i>Da: scalable serverless data analytics and in situ query processing.

Journal of big data·2025
Same journal

Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.

Journal of big data·2024
Same journal

Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.

Journal of big data·2024
Same journal

Multi-sample <math></math>-mixup: richer, more realistic synthetic samples from a <i>p</i>-series interpolant.

Journal of big data·2024
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.7K

独特的NOSD:一个新的框架,用于NoSQL超过SQL数据库.

Abdulrauf A Gidado1, C I Ezeife1

  • 1School of Computer Science, University of Windsor, Windsor, ON N9B 3P4 Canada.

Journal of big data
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个新的NoSQL over SQL Database (UniqueNOSD) 系统,增强大数据库的数据一致性和可扩展性. 我们的方法优化了NoSQL数据存储和检索,没有冗余,超过现有系统的性能.

关键词:
大数据的大数据大数据这是一个NoSQL数据库.在SQL上使用NoSQL.关系数据库是一个关系数据库.在NoSQL上使用SQL.

更多相关视频

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.7K
Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K

相关实验视频

Last Updated: Jan 10, 2026

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.7K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.7K
Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

1.6K

科学领域:

  • 数据库系统和数据库管理
  • 分布式计算 (Distributed Computing) 是一种分布式计算.
  • 数据存储和检索数据

背景情况:

  • 大型企业主要使用关联数据库用于核心系统和非关联 (NoSQL) 数据库用于非核心系统,优先考虑可用性和可扩展性而不是一致性.
  • 基于CAP定理的NoSQL系统经常以可用性和分区耐受性换取一致性,由于缺乏强大的查询引擎,限制了它们的使用.
  • 现有的"SQL over NoSQL"解决方案引入了查询层,但存在数据冗余和一致性问题,并且在关系上不完整.

研究的目的:

  • 介绍一个"在SQL数据库上独一无二的NoSQL" (UniqueNOSD) 系统,这是对现有方法的反向方法,在没有额外的SQL查询层的情况下实现完整的NoSQL功能.
  • 为基于文档的NoSQL数据库提出NoSQL over SQL Block作为一个价值数据存储策略,旨在消除数据冗余和不一致.
  • 在大型数据库系统中展示改进的数据存储,检索,查询执行,一致性和可扩展性.

主要方法:

  • 引入了一个"NoSQL over SQL Block as a Value" (B as V) 数据存储策略,将关系表示为[公式:参见文本] (一个元组 (K,B)) 而不是传统的[公式:参见文本]模型.
  • 定义关系为 [公式:见文本] 具有关键属性 K 和一组 n 个关系 (块) B,其中每个块 [公式:见文本] 包含其自己的属性,并通过外键相关.
  • 通过使用'SQL over NoSQL',关系数据库和现实数据集的基准系统进行实验,以评估UniqueNOSD系统.

主要成果:

  • 与现有的关系数据库和"SQL over NoSQL"系统相比,UniqueNOSD系统表现出优越的性能.
  • 拟议的系统确保了数据的一致性,可扩展性和高效的查询执行.
  • 在数据存储和检索方面观察到显著的改进,没有数据丢失,提高了NoSQL数据库的整体性能.

结论:

  • 独特的NoSQL对SQL数据库 (UniqueNOSD) 系统有效地解决了传统NoSQL和"SQL对NoSQL"方法的局限性.
  • "NoSQL over SQL Block as a Value"策略为NoSQL数据库中的一致和可扩展的数据管理提供了一个新的解决方案.
  • 这些发现表明,UniqueNOSD对于需要NoSQL灵活性和关系数据完整性的大规模数据库系统来说是一个可行和高性能的替代方案.