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Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), [1] and deriving a conclusion from them. Complex event processing ( CEP ) consists of a set of concepts and techniques developed in the early 1990s for processing real-time events and extracting information from ...
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. [6]
A data processing system is a combination of machines, people, and processes that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data , facts , information etc. depending on the interpreter's relation to the system.
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
Also simply application or app. Computer software designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. Common examples of applications include word processors, spreadsheets, accounting applications, web browsers, media players, aeronautical flight simulators, console games, and photo editors. This contrasts with system software, which is ...
Data science is a field that uses scientific and computing tools to extract information and insights from data, driven by the increasing volume and availability of data. [46] Data mining , big data , statistics, machine learning and deep learning are all interwoven with data science.
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In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data. [8] The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable ...