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Related Concept Videos

Review and Preview01:13

Review and Preview

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Data Reporting and Recording01:24

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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...
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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.
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Data Collection III01:05

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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.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Data Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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A review of data abstraction.

Gianluca Cima1, Marco Console1, Maurizio Lenzerini1

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Data abstraction is crucial for effective Artificial Intelligence (AI) and Machine Learning (ML) by ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR). This review explores data abstraction

Keywords:
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Management

Background:

  • Effective Artificial Intelligence (AI) and Machine Learning (ML) depend heavily on robust data preparation.
  • Modern data often exists in distributed and heterogeneous sources, complicating data collection.
  • The FAIR guiding principles (Findable, Accessible, Interoperable, Reusable) are essential for data services.

Purpose of the Study:

  • To review existing research on data abstraction techniques.
  • To present a formal framework for defining data abstraction.
  • To discuss the decidability and complexity of data abstraction problems and outline future research directions.

Main Methods:

  • Reviewing theoretical frameworks for data abstraction.
  • Analyzing the decidability and complexity of core problems in data abstraction.
  • Synthesizing current research findings and identifying open issues.

Main Results:

  • Data abstraction provides semantic characterization of data services through reverse engineering.
  • Formal frameworks for data abstraction have been developed.
  • Decidability and complexity results for key abstraction problems are reported.

Conclusions:

  • Data abstraction is a vital technique for addressing challenges in distributed data integration.
  • The formalization of data abstraction aids in making data services FAIR compliant.
  • Further research is needed to advance data abstraction methods and applications.