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Handbook of data quality : research and practice

Autor: Shazia Sadiq
Editorial: Berlin ; New York : Springer-Verlag, ©2013.
Edición/Formato:   Libro-e : Documento : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
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  Leer más
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Detalles

Género/Forma: Electronic books
Handbooks, manuals, etc
Handbooks
Formato físico adicional: Print version:
Handbook of Data Quality.
Berlin Springer-Verlag, 2013
(OCoLC)842411114
Tipo de material: Documento, Recurso en Internet
Tipo de documento: Recurso en Internet, Archivo de computadora
Todos autores / colaboradores: Shazia Sadiq
ISBN: 9783642362576 3642362575
Número OCLC: 843180440
Notas: Includes index.
Descripción: 1 online resource (xii, 438 p.) : ill.
Contenido: Organizational Aspects of Data Quality. Data Quality Management Past, Present, and Future: Towards a Management System for Data / Thomas C. Redman --
Data Quality Projects and Programs / Danette McGilvray --
Cost and Value Management for Data Quality / Mouzhi Ge, Markus Helfert --
On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America / Boris Otto --
Architectural Aspects of Data Quality. Data Warehouse Quality: Summary and Outlook / Lukasz Golab --
Using Semantic Web Technologies for Data Quality Management / Christian Fürber, Martin Hepp --
Data Glitches: Monsters in Your Data / Tamraparni Dasu --
Computational Aspects of Data Quality. Generic and Declarative Approaches to Data Quality Management / Leopoldo Bertossi, Loreto Bravo --
Linking Records in Complex Context / Pei Li, Andrea Maurino --
A Practical Guide to Entity Resolution with OYSTER / John R. Talburt, Yinle Zhou --
Managing Quality of Probabilistic Databases / Reynold Cheng --
Data Fusion: Resolving Conflicts from Multiple Sources / Xin Luna Dong, Laure Berti-Equille, Divesh Srivastava --
Data Quality in Action. Ensuring the Quality of Health Information: The Canadian Experience / Heather Richards, Nancy White --
Shell's Global Data Quality Journey / Ken Self --
Creating an Information-Centric Organisation Culture at SBI General Insurance / Ram Kumar, Robert Logie.
Responsabilidad: edited by Shazia Sadiq.

Resumen:

This multi-pronged approach to data quality management covers Organization: processes, policies and standards needed to set data quality objectives; Architecture: the technological landscape for  Leer más

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From the reviews: "The book is suitable for academics and students in computer science, information systems, and management. Practitioners should find matters related to their practice areas in one Leer más

 
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Datos enlazados


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schema:description"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."@en
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