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Related Concept Videos

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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Related Experiment Video

Updated: Jul 23, 2026

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ELI: an IoT-aware big data pipeline with data curation and data quality.

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
Summary

This study introduces ELI, an IoT Big Data pipeline for data curation and quality assessment. It ensures reliable decision-making by identifying and removing low-quality IoT data in real-time and offline scenarios.

Keywords:
Big data pipelineData curationData qualityInternet of ThingsSensors

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Area of Science:

  • Data Science
  • Internet of Things (IoT)
  • Big Data Analytics

Background:

  • Analyzing IoT sensor data requires Big Data technologies, presenting challenges in data curation and quality assessment.
  • Poor data quality can lead to erroneous decision-making, increased costs, and process errors.

Purpose of the Study:

  • To present ELI, an IoT-based Big Data pipeline for data curation and usability assessment.
  • To address challenges in analyzing complex IoT sensor data for reliable decision-making.

Main Methods:

  • Developed an IoT-based Big Data pipeline integrating data transformation and integration tools.
  • Implemented a customizable Decision Model and Notation (DMN) model for data quality evaluation.
  • Evaluated the pipeline in a smart farm scenario using agricultural humidity and temperature data.

Main Results:

  • The ELI pipeline effectively performs data curation and assesses data usability in both offline and online (stream data) scenarios.
  • Consistent results were observed across offline and online data streams.
  • Performance evaluation demonstrated the pipeline's effectiveness in identifying and discarding low-quality data.

Conclusions:

  • Data curation and quality assessment are crucial for integrating IoT information and enabling meaningful insights.
  • The proposed ELI pipeline offers a usable and effective solution for managing IoT Big Data quality.
  • Customizable decision models enhance data quality measurement across multiple dimensions.