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Data Cleaning by Venkatesh Ganti (English) Paperback Book
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Specificaties
- Objectstaat
- ISBN-13
- 9783031007699
- Book Title
- Data Cleaning
- ISBN
- 9783031007699
- Subject Area
- Computers
- Publication Name
- Data Cleaning
- Publisher
- Springer International Publishing A&G
- Item Length
- 9.3 in
- Subject
- Information Theory, Networking / General, Databases / General
- Publication Year
- 2013
- Series
- Synthesis Lectures on Data Management Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Weight
- 6.5 Oz
- Item Width
- 7.5 in
- Number of Pages
- Xv, 69 Pages
Over dit product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3031007697
ISBN-13
9783031007699
eBay Product ID (ePID)
24057271019
Product Key Features
Number of Pages
Xv, 69 Pages
Language
English
Publication Name
Data Cleaning
Publication Year
2013
Subject
Information Theory, Networking / General, Databases / General
Type
Textbook
Subject Area
Computers
Series
Synthesis Lectures on Data Management Ser.
Format
Trade Paperback
Dimensions
Item Weight
6.5 Oz
Item Length
9.3 in
Item Width
7.5 in
Additional Product Features
Number of Volumes
1 vol.
Illustrated
Yes
Original Language
English
Table Of Content
Preface.- Acknowledgments.- Introduction.- Technological Approaches.- Similarity Functions.- Operator: Similarity Join.- Operator: Clustering.- Operator: Parsing.- Task: Record Matching.- Task: Deduplication.- Data Cleaning Scripts.- Conclusion.- Bibliography.- Authors' Biographies.
Synopsis
Data warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.
LC Classification Number
TK5105.5-5105.9
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