Data Quality And High Dimensional Data Analysis
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| Author | : Chee-Yong Chan |
| Publisher | : World Scientific |
| Total Pages | : 117 |
| Release | : 2009 |
| Genre | : Computers |
| ISBN | : 981427349X |
Poor data quality is known to compromise the credibility and efficiency of commercial and public endeavours. Also, the importance of managing data quality has increased manifold as the diversity of sources, formats and volume of data grows. This volume targets the data quality in the light of collaborative information systems where data creation and ownership is increasingly difficult to establish.
| Author | : Tianwen Tony Cai |
| Publisher | : World Scientific Publishing Company Incorporated |
| Total Pages | : 307 |
| Release | : 2011 |
| Genre | : Mathematics |
| ISBN | : 9789814324854 |
Over the last few years, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from high-dimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research. The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data analysis.
| Author | : Carlo Batini |
| Publisher | : Springer |
| Total Pages | : 520 |
| Release | : 2016-03-23 |
| Genre | : Computers |
| ISBN | : 3319241060 |
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.
| Author | : Chee-yong Chan |
| Publisher | : World Scientific |
| Total Pages | : 117 |
| Release | : 2009-02-19 |
| Genre | : Computers |
| ISBN | : 9814467901 |
Poor data quality is known to compromise the credibility and efficiency of commercial and public endeavours. Also, the importance of managing data quality has increased manifold as the diversity of sources, formats and volume of data grows. This volume targets the data quality in the light of collaborative information systems where data creation and ownership is increasingly difficult to establish.
| Author | : Rupa Mahanti |
| Publisher | : Quality Press |
| Total Pages | : 526 |
| Release | : 2019-03-18 |
| Genre | : Business & Economics |
| ISBN | : 0873899776 |
This is not the kind of book that youll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph.D., the Data Doc Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.
| Author | : John Wright |
| Publisher | : Cambridge University Press |
| Total Pages | : 717 |
| Release | : 2022-01-13 |
| Genre | : Computers |
| ISBN | : 1108489737 |
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
| Author | : Yng-Yuh Richard Wang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 175 |
| Release | : 2001 |
| Genre | : Business & Economics |
| ISBN | : 0792372158 |
Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.
| Author | : Carlo Batini |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 276 |
| Release | : 2006-09-27 |
| Genre | : Computers |
| ISBN | : 3540331735 |
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.
| Author | : Thomas C. Redman |
| Publisher | : Digital Press |
| Total Pages | : 264 |
| Release | : 2001 |
| Genre | : Computers |
| ISBN | : 9781555582517 |
Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality. Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it. The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points. Allows readers to go directly to the topic of interest Provides web-based material so readers can cut and paste figures and tables into documents within their organizations Gives step-by-step instructions for applying most techniques and summarizes what "works"
| Author | : Nathan Gaw |
| Publisher | : Springer Nature |
| Total Pages | : 388 |
| Release | : |
| Genre | : |
| ISBN | : 3031530926 |
