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相关概念视频

Data Collection by Observations01:08

Data Collection by Observations

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...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Collection II01:29

Data Collection II

The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and family,...
Data Collection III01:05

Data Collection III

The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the patient.
Data Reporting and Recording01:24

Data Reporting and Recording

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...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

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相关实验视频

Updated: Jul 23, 2026

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures
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ELI:一个物联网意识的大数据管道,具有数据策划和数据质量.

Francisco José de Haro-Olmo1, Alvaro Valencia-Parra2, Ángel Jesús Varela-Vaca2

  • 1Departamento de Informática, Universidad de Almería, Almería, Spain.

PeerJ. Computer science
|October 9, 2023
PubMed
概括

本研究介绍了ELI,这是一个物联网大数据管道,用于数据策划和质量评估. 它通过在实时和离线场景中识别和删除低质量的物联网数据,确保可靠的决策.

关键词:
大数据管道的大数据管道.数据策划数据的策划.数据质量数据质量数据质量物联网的物联网,就是物联网.传感器 传感器 传感器

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科学领域:

  • 数据科学数据科学数据科学
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.
  • 大数据分析大数据分析

背景情况:

  • 分析物联网传感器数据需要大数据技术,在数据策划和质量评估方面提出了挑战.
  • 数据质量不佳可能导致错误的决策,成本增加和流程错误.

研究的目的:

  • 介绍ELI,一个基于物联网的大数据管道,用于数据策划和可用性评估.
  • 解决分析复杂物联网传感器数据的挑战,以便可靠的决策.

主要方法:

  • 开发了一个基于物联网的大数据管道,集成数据转换和集成工具.
  • 实现了可定制的决策模型和标记 (DMN) 模型用于数据质量评估.
  • 在智能农场场景中使用农业湿度和温度数据评估管道.

主要成果:

  • ELI管道有效地执行数据策划,并在线下和线上 (流数据) 场景中评估数据可用性.
  • 在线和离线数据流中观察到一致的结果.
  • 绩效评估证明了管道在识别和丢弃低质量的数据方面的有效性.

结论:

  • 数据策划和质量评估对于整合物联网信息和实现有意义的见解至关重要.
  • 拟议的ELI管道为管理物联网大数据质量提供了可用的和有效的解决方案.
  • 可定制的决策模型增强了跨多个维度的数据质量测量.