This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.
Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.
Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
This book is part of a concentrated series of books that examines child maltreatment across cultural groups. Specifically, this volume examines core concepts relevant to Latinx families (e.g., familismo, acculturation, spirituality, oppression) as they relate to child maltreatment in the United States. While there are vast differences across Latinx families, authors use critical race and feminist theories to explore the impact of differences based on gender, race, immigration status, and country of origin. The book begins by contextualizing child maltreatment in Latinx families within the pervasive structural racism and inequality in the United States and addressing unique traumas experienced by Latinx families resulting from that inequity. Subsequent chapters address prevention of child maltreatment, responses to maltreatment and healing from trauma with an emphasis on resilience within the Latinx community. Three case studies are used to illustrate and apply concepts from each chapter.
Child Abuse and Neglect examines the latest research on this important topic, discussing what it entails, how to recognize it, and how to report it. The book begins with an overview of child maltreatment including its history, a summary of the research, and the risk factors, before exploring issues of mandated reporting. It then considers different forms of maltreatment – physical abuse, neglect, psychological maltreatment, sexual abuse, fetal abuse, and Munchausen by Proxy Syndrome. The authors discuss incidence estimates and consequences, as well as resiliency, for each type of maltreatment, and then review legal issues including forensic interviewing. The book concludes by providing an overview of what happens to a child after a report is filed along with suggestions for preventing child maltreatment. This edition has been thoroughly updated throughout to cover the latest theory and research. Referencing the DSM-V, the book also features updated coverage of state and federal laws to reflect new legislation, and additional case studies covering real-world events such as the sexual abuse scandals within USA Gymnastics, the Boy Scouts of America, and the Southern Baptist Convention. Written with students in mind, the book features a wealth of engaging learning tools throughout, including: Theory Highlight boxes, Focus on Research boxes, Case Examples, Legal Examples, Focus on Law boxes, Discussion Questions, and Key Terms. It will be essential reading for all students taking courses on child abuse, child maltreatment, family violence, or sexual and intimate violence taught in psychology, human development, education, criminal justice, social work, sociology, women’s studies, and nursing. This book will also be an invaluable resource to workers who are mandated reporters of child maltreatment and/or anyone interested in the problem. This book is based on the legal system and the Child Protection System in the United States of America. It is accompanied by a set of online instructor resources.
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