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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.
Data collection, preparation and mining all belong to the phase in which the profile is under construction. However, profiling also refers to the application of profiles, meaning the usage of profiles for the identification or categorization of groups or individual persons. As can be seen in step six (application), the process is circular.
Data profiling - initially assessing the data to understand its current state, often including value distributions; Data standardization - a business rules engine that ensures that data conforms to standards; Geocoding - for name and address data. Corrects data to U.S. and Worldwide geographic standards
These trends can be identified by analyzing data gained through surveys, censuses, in-store purchase information, records, registries, and so on. [2] Analysis of this information may promote change in services for a population subset, such as children, the elderly, or working-age people. [ 1 ]
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.
Profiling, the extrapolation of information about something, ... Data profiling; Forensic profiling, used in several types of forensic science; Offender profiling;
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]
Profiling results can be used to guide the design and optimization of an individual algorithm; the Krauss matching wildcards algorithm is an example. [5] Profilers are built into some application performance management systems that aggregate profiling data to provide insight into transaction workloads in distributed applications. [6]
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