Bebe Barrow
2018-12-03 18:32:56 UTC
I am pleased to announce the latest title in Morgan & Claypools series on Data Management:
Data Profiling
Ziawasch Abedjan, Technische Universität Berlin
Lukasz Golab, University of Waterloo
Felix Naumann, Hasso Plattner Institute, University of Potsdam
Thorsten Papenbrock, Hasso Plattner Institute, University of Potsdam
Paperback ISBN: 9781681734460
eBook ISBN: 9781681734477
Hardcover ISBN: 9781681734484
November 2018, 154 pages
http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1328
Abstract:
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.
Table of Contents: 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
Series: Synthesis Lectures on Data Management
Editors: H. V. Jagadish, University of Michigan
http://www.morganclaypoolpublishers.com/catalog_Orig/index.php?cPath=22&sort=2d&series=17
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Data Profiling
Ziawasch Abedjan, Technische Universität Berlin
Lukasz Golab, University of Waterloo
Felix Naumann, Hasso Plattner Institute, University of Potsdam
Thorsten Papenbrock, Hasso Plattner Institute, University of Potsdam
Paperback ISBN: 9781681734460
eBook ISBN: 9781681734477
Hardcover ISBN: 9781681734484
November 2018, 154 pages
http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1328
Abstract:
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.
Table of Contents: 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
Series: Synthesis Lectures on Data Management
Editors: H. V. Jagadish, University of Michigan
http://www.morganclaypoolpublishers.com/catalog_Orig/index.php?cPath=22&sort=2d&series=17
_______________________________________________
Please do not post msgs that are not relevant to the database community at large. Go to www.cs.wisc.edu/dbworld for guidelines and posting forms.
To unsubscribe, go to https://lists.cs.wisc.edu/mailman/listinfo/dbworld