The phrase "abstraction of the data" is correct and usable in written English.
It can be used in contexts related to data analysis, computer science, or information theory, where one discusses the process of simplifying complex data into a more understandable form.
Example: "The abstraction of the data allows us to focus on the key trends without getting lost in the details."
Alternatives: "data simplification" or "data representation".
The sliding window model [2] is a commonly used data stream model, which can improve the processing efficiency of the data stream by logical abstraction of the data stream.
Abstraction of the data took place prior to screening for industry associations and reviewers were blinded with regard to knowledge about the researchers' industry associations.
EHR carried out the Delphi-study, drafted the manuscript and performed statistical analysis and abstraction of the data.
In step 6, we synthesised the translations in each key concept to develop third-order interpretations, or higher levels of abstraction of the data for each key concept.
PL resolved the dicrepancies between the two reviewers (XX and DC) in the abstraction of the data from the study of interest.
While categories are closely and explicitly linked to the raw data, developing categories is a way to start the process of abstraction of the data (i.e. towards the general rather than the specific or anecdotal).
The proposed functional holonic structure is suitable for processing any level of information abstraction of the Joint Directors of Laboratory JDLL) data fusion model.
The average time spent on data collection was 8.5 days per hospital for the five QIs related to medical or anaesthetic record content, and 5 days for AMI QIs (including sample of the medical records, retrieval from archives, abstraction of the sample, data entry in the computer and verification of data quality).
In data abstraction, details of the data container and the data elements may not be visible to the consumer of the data.
Our main contribution is on investigating clustering algorithms to find the reduction (abstraction) rate of the data, correlation between the population and sample, explanatory power, the computational complexity, and the memory usage.
The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well.
