Data profiling
著者
書誌事項
Data profiling
（Synthesis lectures on data management, #52）
Morgan & Claypool, c2019
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注記
Other authors: Lukasz Golab, Felix Naumann, Thorsten Papenbrock
Includes bibliographical references (p. 113-134)
DOI:10.2200/S00878ED1V01Y201810DTM052
内容説明・目次
内容説明
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.
目次
Preface
Acknowledgments
Discovering Metadata
Data Profiling Tasks
Single Column Analysis
Dependency Discovery
Relaxed and Other Dependencies
Use Cases
Profiling Non-Relational Data
Data Profiling Tools
Data Profiling Challenges
Conclusions
Bibliography
Authors' Biographies
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