Data cleaning de ihab ilyas (44 resultados)

Condición: very_good. This is a well-cared-for used book with light signs of previous use. There may be minor cover wear, a faint crease, or slight spine wear, but overall it's in great shape and fully readable. Please note: -May contain library or rental stickers. -Supplemental materials e.g., CDs, access codes, inserts are not guaranteed. -Box sets may not include original exterior box. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading. …

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Condición: New. pp. 282.

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Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

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Condición: New. pp. 282.

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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condición: New. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions.Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems.This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, we give an overview of the end-to-end data cleaning process, describing various error detection and repair methods, and attempt to anchor these proposals with multiple taxonomies and views. Specifically, we cover four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, we include a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models.This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate. …

Condición: New. In English.

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Paperback. Condición: Brand New. 282 pages. 9.25x7.50x0.75 inches. In Stock.

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Condición: New. In English.

Taschenbuch. Condición: Neu. Data Cleaning | Ihab F. Ilyas (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | ACM Books | EAN 9781450371537 | Verantwortliche Person für die EU: Mare Nostrum Group B.V., Doelen 72, 4831 GR BREDA, NIEDERLANDE, gpsr[at]mare-nostrum[dot]co[dot]uk | Anbieter: preigu.…

Hardback. Condición: New. This is an overview of the end-to-end data cleaning process.Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models.This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate. …

Hardcover. Condición: Brand New. 260 pages. 9.50x7.75x1.00 inches. In Stock.

Paperback. Condición: New. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions.Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems.This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, we give an overview of the end-to-end data cleaning process, describing various error detection and repair methods, and attempt to anchor these proposals with multiple taxonomies and views. Specifically, we cover four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, we include a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models.This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate. …