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Data Profiling (Paperback)
Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock
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R1,494
Discovery Miles 14 940
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Ships in 18 - 22 working days
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Data profiling refers to the activity of collecting data about
data, {i.e.}, metadata. Most IT professionals and researchers who
work with data have engaged in data profiling, at least informally,
to understand and explore an unfamiliar dataset or to determine
whether a new dataset is appropriate for a particular task at hand.
Data profiling results are also important in a variety of other
situations, including query optimization, data integration, and
data cleaning. Simple metadata are statistics, such as the number
of rows and columns, schema and datatype information, the number of
distinct values, statistical value distributions, and the number of
null or empty values in each column. More complex types of metadata
are statements about multiple columns and their correlation, such
as candidate keys, functional dependencies, and other types of
dependencies. This book provides a classification of the various
types of profilable metadata, discusses popular data profiling
tasks, and surveys state-of-the-art profiling algorithms. While
most of the book focuses on tasks and algorithms for relational
data profiling, we also briefly discuss systems and techniques for
profiling non-relational data such as graphs and text. We conclude
with a discussion of data profiling challenges and directions for
future work in this area.
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