[PDF] Data Quality Management With Semantic Technologies eBook
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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.
Author : Richard Y. Wang Publisher : Springer Science & Business Media Page : 175 pages File Size : 47,22 MB Release : 2006-04-11 Category : Computers ISBN : 0306469871
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.
Provides an overview of fundamental issues underlying central aspects of data quality - data consistency, data deduplication, data accuracy, data currency, and information completeness. The book promotes a uniform logical framework for dealing with these issues, based on data quality rules.
Author : Thomas C. Redman Publisher : Random House Puzzles & Games Page : 308 pages File Size : 49,81 MB Release : 1992 Category : Computers ISBN : 9780553091496
Data Quality begins with an explanation of what data is, how it is created and destroyed, then explores the true quality of data--accuracy, consistency and currentness. From there, the author covers the powerful methods of statistical quality control and process management to bear on the core processes that create, manipulate, use and store data values. Table of Contents: 1. Introduction; 2. Data and Information; 3. Dimensions of Data Quality; 4. Statistical Quality Control; 5. Process Management; 6. Process Representation and the Functions of Information Processing Approach; 7. Data Quality Requirements; 8. Measurement Systems and Data Quality; 9. Process Redesign Using Experimentation and Computer Simulation; 10. Managing Multiple Processes; 11. Perspective Prospects and Implications; 12. Summaries.
The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.
Provides a single record of technologies and practices of the Semantic approach to the management, organization, interpretation, retrieval, and use of Web-based data.
The two-volume set LNCS 7649 + 7650 constitutes the refereed proceedings of the 11th International Semantic Web Conference, ISWC 2012, held in Boston, MA, USA, in November 2012. The International Semantic Web Conference is the premier forum for Semantic Web research, where cutting edge scientific results and technological innovations are presented, where problems and solutions are discussed, and where the future of this vision is being developed. It brings together specialists in fields such as artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human-computer interaction, natural language processing, and the social sciences. Volume 1 contains a total of 41 papers which were presented in the research track. They were carefully reviewed and selected from 186 submissions. Volume 2 contains 17 papers from the in-use track which were accepted from 77 submissions. In addition, it presents 8 contributions to the evaluations and experiments track and 7 long papers and 8 short papers of the doctoral consortium.
Collaborative working has been increasingly viewed as a good practice for organizations to achieve efficiency. Organizations that work well in collaboration may have access to new sources of funding, deliver new, improved, and more integrated services, make savings on shared costs, and exchange knowledge, information and expertise. Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources showcases cutting-edge research on the intersections of Semantic Web, collaborative work, and social media research, exploring how the resources of so-called social networking applications, which bring people together to interact and encourage sharing of personal information and ideas, can be tapped by Semantic Web techniques, making shared Web contents readable and processable for machine and intelligent applications, as well as humans. Semantic technologies have shown their potential for integrating valuable knowledge, and they are being applied to the composition of digital learning and working platforms. Integrated semantic applications, linked data, social networks, and networked digital solutions can now be used in collaborative environments and present participants with the context-aware information that they need.
What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.