LEADER 03376nam 22004573 450 001 9910838222203321 005 20230817184958.0 010 $a1-4503-7155-8 035 $a(CKB)4100000011919183 035 $a(MiAaPQ)EBC6954840 035 $a(Au-PeEL)EBL6954840 035 $a(OCoLC)1321789672 035 $a(EXLCZ)994100000011919183 100 $a20220421d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Cleaning 210 1$aSan Rafael :$cMorgan & Claypool Publishers,$d2019. 210 4$dİ2019. 215 $a1 online resource (284 pages) 327 $aIntro -- Contents -- Preface -- Figure and Table Credits -- 1. Introduction -- 2. Outlier Detection -- 3. Data Deduplication -- 4. Data Transformation -- 5. Data Quality Rule Definition and Discovery -- 6. Rule-Based Data Cleaning -- 7. Machine Learning and Probabilistic Data Cleaning -- 8. Conclusion and Future Thoughts -- References -- Index -- Author Biographies -- Blank Page. 330 $aThis 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. 606 $aData editing 606 $aDatabase management 606 $aElectronic data processing 615 0$aData editing. 615 0$aDatabase management. 615 0$aElectronic data processing. 676 $a005.74 700 $aIlyas$b Ihab F$01730042 701 $aChu$b Xu$01730043 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910838222203321 996 $aData Cleaning$94140423 997 $aUNINA