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Integrity Constraints on Rich Data Types

Title
Integrity Constraints on Rich Data Types [electronic resource] / by Shaoxu Song, Lei Chen.
ISBN
9783031271779
Edition
1st ed. 2023.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2023.
Physical Description
1 online resource (XI, 146 p.) 35 illus., 14 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book examines the recent trend of extending data dependencies to adapt to rich data types in order to address variety and veracity issues in big data. Readers will be guided through the full range of rich data types where data dependencies have been successfully applied, including categorical data with equality relationships, heterogeneous data with similarity relationships, numerical data with order relationships, sequential data with timestamps, and graph data with complicated structures. The text will also discuss interesting constraints on ordering or similarity relationships contained in novel classes of data dependencies in addition to those in equality relationships, e.g., considered in functional dependencies (FDs). In addition to exploring the concepts of these data dependency notations, the book investigates the extension relationships between data dependencies, such as conditional functional dependencies (CFDs) that extend conventional functional dependencies (FDs). This forms in the book a family tree of extensions, mostly rooted in FDs, that help illuminate the expressive power of various data dependencies. Moreover, the book points to work on the discovery of dependencies from data, since data dependencies are often unlikely to be manually specified in a traditional way, given the huge volume and high variety in big data. It further outlines the applications of the extended data dependencies, in particular in data quality practice. Altogether, this book provides a comprehensive guide for readers to select proper data dependencies for their applications that have sufficient expressive power and reasonable discovery cost. Finally, the book concludes with several directions of future studies on emerging data.
Variant and related titles
Springer Nature Synthesis Collection of Technology.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 05, 2023
Series
Synthesis lectures on data management.
Synthesis Lectures on Data Management,
Contents
Introduction
Categorical Data
Heterogeneous Data
Ordered Data
Temporal Data
Graph Data
Conclusions and Directions
Index of Data Dependencies
References.
Also listed under
Chen, Lei, author.
SpringerLink (Online service)
Citation

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