الصفحة 1
الصفحة 1
How Data Quality Affects our Understanding of the Earnings Distribution
This book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error.
Healthy Longevity in China : Demographic, Socioeconomic, and Psychological Dimensions
China is aging at an extraordinary speed and has the largest quantity of elderly persons in the world. Scholars utilize this unprecedented living experience of human being and the unique Chinese Longitudinal Healthy Longevity Survey (CLHLS) datasets with the aim to answer in this volume the following questions critical to the aging population world wide. Is the period of disability compressing or expanding with increasing life expectancy and what factors are associated with these trends in the recent decades? Is it possible to realize morbidity compression with a prolongation of the life span in the future?The first section of the book presents the CLHLS project’s study design, sample distribution, contents of data collected, and assessments of age reporting and data quality. The remaining chapters are grouped into sections dealing with the demographic, social, economic, familial and psychological dimensions of healthy longevity.
Data Quality and Record Linkage Techniques
This book helps practitioners gain a deeper understanding, at an applied level, of the issues involved in improving data quality through editing, imputation, and record linkage. The first part of the book deals with methods and models. Here, we focus on the Fellegi-Holt edit-imputation model, the Little-Rubin multiple-imputation scheme, and the Fellegi-Sunter record linkage model. Brief examples are included to show how these techniques work. In the second part of the book, the authors present real-world case studies in which one or more of these techniques are used. They cover a wide variety of application areas. These include mortgage guarantee insurance, medical, biomedical, highway safety, and social insurance as well as the construction of list frames and administrative lists.
Data Quality : Concepts, Methodologies and Techniques
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.
Data Integration in the Life Sciences ; Vol. 4075 ; 3rd International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings
Data management and data integration are fundamental problems in the life sciences. Advances in molecular biology and molecular medicine are almost u- versallyunderpinned by enormouse?orts in data management,data integration, automatic data quality assurance, and computational data analysis. Many hot topics in the life sciences, such as systems biology, personalized medicine, and pharmacogenomics, critically depend on integrating data sets and applications producedby di?erent experimentalmethods, in di?erent researchgroups,andat di?erent levels of granularity.
Corporate Data Quality : Voraussetzung erfolgreicher Geschäftsmodelle
Data is the basis of the digitized economy. Industry 4.0 and digital services produce unprecedented amounts of data and enable new business models. Data quality is becoming a critical success factor. This book presents an approach to quality-oriented data management and ten case studies for practitioners and scientists alike. The book was created in the Competence Center Corporate Data Quality in close cooperation between researchers from the University of St. Gallen and the Fraunhofer IML as well as numerous representatives from more than twenty large companies.
Managing Information Quality : Increasing the Value of Information in Knowledge-intensive Products and Processes
It still holds true: information is not always the answer. Information is often part of the problem. While the main goal of information in the business place is to - able adequate decisions and actions, it can also lead to numerous negative effects: it can confuse, block creativity, or it can lead to hectic activism, stress and fatigue. Information can distract and divert attention, and it may even delay important - cisions – the paralysis by analysis. Strategies to avoid these dysfunctional effects of information can be divided into sender-based strategies and receiver-based strategies.
Linked Open Data -- Creating Knowledge Out of Interlinked Data : Results of the LOD2 Project
Linked Open Data (LOD) is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. This book presents an overview on the results of the research project “LOD2 -- Creating Knowledge out of Interlinked Data”. LOD2 is a large-scale integrating project co-funded by the European Commission within the FP7 Information and Communication Technologies Work Program. Commencing in September 2010, this 4-year project comprised leading Linked Open Data research groups, companies, and service providers from across 11 European countries and South Korea.
Building a Data Warehouse : With Examples in SQL Server
The book is organized as follows. In the beginning of this book (chapters 1 through 6), you learn how to build a data warehouse, for example, defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Then in chapters 7 through 10, you learn how to populate the data warehouse, for example, extracting from source systems, loading the data stores, maintaining data quality, and utilizing the metadata. After you populate the data warehouse, in chapters 11 through 15, you explore how to present data to users using reports and multidimensional databases and how to use the data in the data warehouse for business intelligence, customer relationship management, and other purposes. Chapters 16 and 17 wrap up the book: After you have built your data warehouse, before it can be released to production, you need to test it thoroughly. After your application is in production, you need to understand how to administer data warehouse operation.
Big Data in Context : Legal, Social and Technological Insights
Sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.
