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This research paper presents an innovative automated method for metadata identification and enrichment, aimed at addressing Big Data variety challenges. It emphasizes reducing false positives in confidential data identification.
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How to fill out automated-big-data-characterization

01
Determine the scope and objective of the big data characterization process.
02
Identify the data sources and types of data that need to be characterized.
03
Select an automated big data characterization tool that suits your needs.
04
Configure the tool to connect to the data sources and define the parameters for characterization.
05
Run the automated characterization process and review the results.
06
Analyze the characterization results to gain insights into the data and its quality.

Who needs automated-big-data-characterization?

01
Businesses and organizations dealing with large volumes of data.
02
Data scientists and analysts looking to quickly and accurately assess the characteristics of big data sets.
03
Researchers and academics studying big data phenomena.
04
IT professionals responsible for managing and analyzing big data.
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Automated-big-data-characterization is a process that uses advanced algorithms and machine learning techniques to analyze large datasets and identify their key characteristics, patterns, and relationships.
Entities that generate, process, or manage large sets of data are typically required to file automated-big-data-characterization, including corporations, institutions, and data-driven organizations.
To fill out an automated-big-data-characterization, organizations need to provide necessary data inputs, choose appropriate algorithms for analysis, and submit the generated report according to the specified guidelines and formats.
The purpose of automated-big-data-characterization is to provide insights into the data's structure and content, enhance data governance, and improve decision-making processes through better understanding of data assets.
The information that must be reported includes data types, data sources, data volume, data quality metrics, and insights derived from the analysis, along with any relevant contextual information.
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