Search results
Results From The WOW.Com Content Network
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model.It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
In scientific inquiry and academic research, data fabrication is the intentional misrepresentation of research results. As with other forms of scientific misconduct , it is the intent to deceive that marks fabrication as unethical, and thus different from scientists deceiving themselves .
Principles of data quality can be applied to supply chain data, transactional data, and nearly every other category of data found. For example, making supply chain data conform to a certain standard has value to an organization by: 1) avoiding overstocking of similar but slightly different stock; 2) avoiding false stock-out; 3) improving the ...
It is the violation of scientific integrity: violation of the scientific method and of research ethics in science, including in the design, conduct, and reporting of research. A Lancet review on Handling of Scientific Misconduct in Scandinavian countries provides the following sample definitions, [1] reproduced in The COPE report 1999: [2]
Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [2]
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [ 2 ] Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.
The importance of point-in-time consistency can be illustrated with what would happen if a backup were made without it. Assume Wikipedia's database is a huge file, which has an important index located 20% of the way through, and saves article data at the 75% mark. Consider a scenario where an editor comes and creates a new article at the same time a backup is being performed, which is being ...