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Big data can include structured, unstructured, or combinations of structured and unstructured data. Big data analysis may integrate raw data from multiple sources. The processing of raw data may also involve transformations of unstructured data to structured data. Other possible characteristics of big data are: [41] Exhaustive
Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data. The most important reason for developing data-parallel applications is the potential for scalable performance, and may result in several orders of magnitude performance improvement.
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 analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the ...
Given the fairly static yet voluminous nature of machine-generated data, data owners rely on highly scalable tools to process and analyze the resulting dataset. Almost all machine-generated data is unstructured but then derived into a common structure. [4] Typically, these derived structures contain many data points/columns. With these data ...
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 ]
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.