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Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offer greater statistical power , while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate .
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]
[4] [5] Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Advances in computing technologies have led to the advent of big data, which usually refers to very large quantities of data, usually at the petabyte scale. Using ...
Such data sets are commonly referred to as big data. [36] Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly. [37] [36]
Big data is defined as the algorithm-based analysis of large-scale, distinct digital data for purposes of prediction, measurement, and governance. [6] [7]This involves processing vast amounts of information from various sources, like social media, sensors, or online transactions, using advanced computer programs (algorithms).
Data ecosystems possess three major characteristics: network, platform, and co-evolution. [1] Network loosely refers to the groups of data and technology developers, providers, and resellers. [1] The platform, then, is the service, tool or platform that is collaboratively used by the network of actors. [1]