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Variability is often included as an additional quality of big data. A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and ...
Data-intensive computing is intended to address this need. Parallel processing approaches can be generally classified as either compute-intensive, or data-intensive. [6] [7] [8] Compute-intensive is used to describe application programs that are compute-bound. Such applications devote most of their execution time to computational requirements ...
There is still no consensus on the definition of data science, and it is considered by some to be a buzzword. [34] Big data is a related marketing term. [35] Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations. [36]
Data exists in three states: data at rest, data in transit and data in use. Data within a computer, in most cases, moves as parallel data. Data moving to or from a computer, in most cases, moves as serial data. Data sourced from an analog device, such as a temperature sensor, may be converted to digital using an analog-to-digital converter.
Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, [1] known as the Internet of things. [2] The term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business value. [3]
Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1] [2] Making the data usable usually involves substantial compute and storage, as well as data processing.
Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013) Analysis refers to dividing a whole into its separate components for individual examination. [ 10 ] Data analysis is a process for obtaining raw data , and subsequently converting it into information useful for decision-making by users. [ 1 ]
Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.