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Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition.
In information science, profiling refers to the process of construction and application of user profiles generated by computerized data analysis.. This is the use of algorithms or other mathematical techniques that allow the discovery of patterns or correlations in large quantities of data, aggregated in databases.
Profiling, the extrapolation of information about something, ... Data profiling; Forensic profiling, used in several types of forensic science; Offender profiling;
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
Importantly, data harvested into RN tools can be repurposed, especially if available as Linked Open Data (RDF triples). These RN tools enhance research support activities by providing data for customized, web pages, CV/biosketch generation and data tables for grant proposals.
In the information sciences, an application profile consists of a set of metadata elements, policies, and guidelines defined for a particular application. [1]The elements may come from one or more element sets, thus allowing a given application to meet its functional requirements by using metadata from several element sets - including locally defined sets.
To do online profiling of users and cluster users, marketers and companies can and will access the following kinds of data: gender, the IP address and city of each user through the Facebook Insight page, who "LIKED" a certain user, a page list of all the pages that a person "LIKED" (transaction data), other people that a user follow (even if it ...
Data exploration is typically conducted using a combination of automated and manual activities. [1] [2] [3] Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. [1]