The article presents a textual Big Data analytics solution developed in a real setting as a part of a high-capacity document digitization and storage system. A software based on machine learning techniques performs automated extraction and processing of textual contents. The work focuses on performance and data confidence evaluation and describes the approach to computing a set of indicators for textual data quality. It then presents experimental results.
Data and Process Quality Evaluation in a Textual Big Data Archiving System
M. Fugini;J. Finocchi
2021-01-01
Abstract
The article presents a textual Big Data analytics solution developed in a real setting as a part of a high-capacity document digitization and storage system. A software based on machine learning techniques performs automated extraction and processing of textual contents. The work focuses on performance and data confidence evaluation and describes the approach to computing a set of indicators for textual data quality. It then presents experimental results.File in questo prodotto:
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