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big data, volume - Coggle Diagram
big data
data mining
data-mining tools
ex:query tools,reporingt tools,multidimensional analysis tools, statistical tools, and intelligent agents
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Data acquisition：From the Internet of Things, the Internet. Internet data mainly includes network application data and mobile App application data. Traditional data resources mainly include ERP system, government system, internal system of various companies, etc. These data can be obtained from the corresponding system software. The meteorological and traffic data released on the public platform can be collected through the network.
Data preprocessing：Before using the algorithm for data mining and analysis, the integrity and quality of the data must be checked, and the data that does not meet the standards must be cleaned to ensure that the collected data has a high level of standardization and can meet the requirements of machine learning
Missing eigenvalues, outliers, duplicates of data
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Is the data for a particular feature in the same data set：This duplicate data can be combined or not processed.
Data standardization：overcome the incomparability of data caused by the unit size with various characteristics, and thus improve the accuracy of machine identification.
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data analysis
Cubes of Information
Cube A represents store information (the layers), product information (the rows), and promotion information (the columns)
Cube B represents a slice of information displaying promotion II for all products at all stores
Cube C represents a slice of information displaying promotion III for product B at store 2
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business intelligence
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Four BI benefits categories :
Quantifiable benefits
Indirectly quantifiable benefits
Unpredictable benefits
Intangible benefits
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