Foundations Of Data Quality Management
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Foundations Of Data Quality Management
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Author : Wenfei Fan
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Foundations Of Data Quality Management written by Wenfei Fan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Foundations Of Data Quality Management
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Author : Paul Thomas
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-06-14
Foundations Of Data Quality Management written by Paul Thomas and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-14 with categories.
A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules.
Data Quality
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Author : Carlo Batini
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-27
Data Quality written by Carlo Batini and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-27 with Computers categories.
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.
Foundations Of Data Quality Management
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Author : Wenfei Fan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2012-08-15
Foundations Of Data Quality Management written by Wenfei Fan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-15 with Computers categories.
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Web Information Systems Engineering Wise 2017
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Author : Athman Bouguettaya
language : en
Publisher: Springer
Release Date : 2017-10-01
Web Information Systems Engineering Wise 2017 written by Athman Bouguettaya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-01 with Computers categories.
The two-volume set LNCS 10569 and LNCS 10570 constitutes the proceedings of the 18th International Conference on Web Information Systems Engineering, WISE 2017, held in Puschino, Russia, in October 2017. The 49 full papers and 24 short papers presented were carefully reviewed and selected from 195 submissions. The papers cover a wide range of topics such as microblog data analysis, social network data analysis, data mining, pattern mining, event detection, cloud computing, query processing, spatial and temporal data, graph theory, crowdsourcing and crowdsensing, web data model, language processing and web protocols, web-based applications, data storage and generator, security and privacy, sentiment analysis, and recommender systems.
Advanced Information Systems Engineering
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Author : Matthias Jarke
language : en
Publisher: Springer
Release Date : 2003-05-21
Advanced Information Systems Engineering written by Matthias Jarke and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-21 with Computers categories.
This book constitutes the refereed proceedings of the 11th International Conference on Advanced Information Systems Engineering, CAiSE'99 held in Heidelberg, Germany in June 1999. The 27 revised full papers presented together with 12 short research papers and two invited contributions were carefully selected from a total of 168 submissions. The papers are organized in topical sections on components, information systems management, method engineering, data warehouses, process modeling, CORBA and distributed information systems, workflow systems, heterogeneous databases, and information systems dynamics.
Transactions On Large Scale Data And Knowledge Centered Systems L
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Author : Abdelkader Hameurlain
language : en
Publisher: Springer Nature
Release Date : 2021-12-02
Transactions On Large Scale Data And Knowledge Centered Systems L written by Abdelkader Hameurlain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with Computers categories.
The LNCS journal Transactions on Large-Scale Data and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 50th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains five fully revised selected regular papers. Topics covered include data anonymization, quasi-identifier discovery methods, symbolic time series representation, detection of anomalies in time series, data quality management in biobanks, and the use of multi-agent technology in the design of intelligent systems for maritime transport.
Data Quality Management With Semantic Technologies
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Author : Christian Fürber
language : en
Publisher: Springer
Release Date : 2015-12-11
Data Quality Management With Semantic Technologies written by Christian Fürber and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-11 with Computers categories.
Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.
Foundations And Applications Of Ai In Data Engineering And Healthcare Analytics
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Author : Bhumika Shah Dr. Arun Prakash Agarwal
language : en
Publisher: DeepMisti Publication
Release Date : 2025-02-02
Foundations And Applications Of Ai In Data Engineering And Healthcare Analytics written by Bhumika Shah Dr. Arun Prakash Agarwal and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.
The advent of artificial intelligence (AI) has ushered in a new era of possibilities, transforming industries and redefining how we solve complex problems. Among its most promising applications, data engineering and healthcare analytics stand out as fields where AI’s potential can revolutionize processes, unlock insights, and enhance outcomes. However, realizing this potential requires a solid understanding of foundational concepts, cutting-edge techniques, and their real- world applications. Foundations and Applications of AI in Data Engineering and Healthcare Analytics bridges the gap between theory and practice, offering readers a comprehensive exploration of AI’s role in these critical domains. This book serves as both an introduction for newcomers and an advanced resource for professionals seeking to deepen their expertise. In the realm of data engineering, AI empowers organizations to manage, process, and analyze vast volumes of data with unprecedented efficiency. From intelligent data pipelines to real-time analytics, this book delves into the tools and techniques that make data actionable and impactful. In healthcare analytics, AI is driving breakthroughs that were once thought impossible—predictive modeling for patient care, personalized medicine, and early disease detection, to name a few. By combining case studies, technical insights, and practical guidelines, this book highlights how AI is shaping a smarter, more responsive healthcare ecosystem. As you embark on this journey, you’ll gain not only the technical knowledge required to implement AI solutions but also the critical perspective needed to navigate ethical considerations, regulatory frameworks, and the human impact of these innovations. Whether you are a data engineer, a healthcare professional, a researcher, or an AI enthusiast, this book offers a roadmap to understanding and leveraging AI to solve real-world challenges in data and healthcare. Welcome to a world where AI meets data and healthcare, unlocking possibilities for a better future. Authors
Advances In Visual Informatics
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Author : Halimah Badioze Zaman
language : en
Publisher: Springer
Release Date : 2017-11-13
Advances In Visual Informatics written by Halimah Badioze Zaman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Conference on Advances in Visual Informatics, IVIC 2017, held in Bangi, Malaysia, in November 2017. The keynote and 72 papers presented were carefully reviewed and selected from 130 submissions. The papers are organized in the following topics: Visualization and Data Driven Technology; Engineering and Data Driven Innovation; Data Driven Societal Well-being and Applications; and Data Driven Cyber Security.