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相关概念视频

Schemas01:42

Schemas

11.6K
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.
11.6K
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

112
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
112
Data Reporting and Recording01:24

Data Reporting and Recording

4.7K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.7K
Types of Aggregate Grading01:15

Types of Aggregate Grading

493
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
493
Data: Types and Distribution01:19

Data: Types and Distribution

720
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
720
Data Collection by Observations01:08

Data Collection by Observations

12.0K
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...
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相关实验视频

Updated: Jun 28, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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JsonCurer:基于聚合图表的JSON数据质量管理

Kai Xiong, Xinyi Xu, Siwei Fu

    IEEE transactions on visualization and computer graphics
    |April 16, 2024
    PubMed
    概括

    在JSON中管理数据质量是一项挑战. JsonCurer是一个交互式可视化系统,旨在帮助识别和修复半结构化的JSON数据中的数据质量问题,提高信息可靠性.

    科学领域:

    • 计算机科学 计算机科学
    • 数据管理数据管理
    • 信息可视化 信息可视化

    背景情况:

    • 高质量的数据对于可靠的信息至关重要,但实际数据往往存在质量问题.
    • 现有的数据质量研究主要涉及表格数据,忽视了像JSON这样的半结构化格式.
    • JSON数据的无模式和等级性质使质量问题的发现和解决变得复杂.

    研究的目的:

    • 介绍JsonCurer,一个交互式可视化系统,用于管理JSON数据中的数据质量.
    • 解决半结构化数据中识别和纠正质量问题的挑战.
    • 为数据从业者提供有效的工具,以确保JSON数据集的可靠性.

    主要方法:

    • 根据从业者采访和对119个JSON文件的分析,开发了数据质量问题的分类.
    • 设计了一个模式可视化来呈现结构信息,统计特征和质量问题.
    • 采用基于相似性的聚合技术,创建一个简洁的JSON数据树可视化,结合总结可视化和泡集来说明质量问题.

    主要成果:

    • JsonCurer有效地将JSON数据中的结构信息,统计特征和数据质量问题可视化.
    • 该系统使用简洁的树结构,集结节点和泡集来表示复杂的数据和质量问题.

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    Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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  • 在数据分析和MongoDB质量保证方面的案例研究表明了JsonCurer的有效性和可用性.
  • 结论:

    • JsonCurer为JSON数据提供了一种新的数据质量管理方法.
    • 交互式可视化系统有助于有效发现和纠正数据质量问题.
    • JsonCurer提高了从半结构化数据中获得的信息的可靠性,特别是在数据分析和数据库质量保证方面.