Wenfei Fan

Jianmin Wang

Zhiyong Peng

Xiaoyong Du

Mohamed A. Sharaf

This book constitutes the proceedings of the 13th Asia-Pacific Conference APWeb 2011 held in conjunction with the APWeb 2011 Workshops XMLDM and USD, in Beijing, China, in April 2011. The 26 full papers presented together with 10 short papers, 3 keynote talks, and 4 demo papers were carefully reviewed and selected from 104 submissions. The submissions range over a variety of topics such as classification and clustering; spatial and temporal databases; personalization and recommendation; data analysis and application; Web mining; Web search and information retrieval; complex and social networks; and secure and semantic Web.

13th Asia-Pacific Web Conference, APWeb 2011, Beijing, Chiina, April 18-20, 2011. Proceedings

Web Technologies and Applications

Jun Yang

Zhaohui Wu

This book constitutes the refereed proceedings of the 6th International Conference on Web-Age Information Management, WAIM 2005, held in Hangzhou, China, in October 2005. The 48 revised full papers, 50 revised short papers and 4 industrial papers presented together with 3 invited contributions were carefully reviewed and selected from 486 submissions. The papers are organized in topical sections on XML, performance and query evaluation, data mining, semantic Web and Web ontology, data management, information systems, Web services and workflow, data grid and database languages, agent and mobile data, database application and transaction management, and 3 sections with industrial, short, and demonstration papers.

6th International Conference, WAIM 2005, Hangzhou, China, October 11-13, 2005, Proceedings

Advances in Web-Age Information Management

Floris Geerts

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

Leonid Libkin

Limsoon Wong

Val Tannen

Wang-Chiew Tan

Michael Fourman

This Festschrift volume, published in honour of Peter Buneman, contains contributions written by some of his colleagues, former students, and friends. In celebration of his distinguished career a colloquium was held in Edinburgh, Scotland, 27-29 October, 2013. The articles presented herein belong to some of the many areas of Peter's research interests.

Essays dedicated to Peter Buneman

In Search of Elegance in the Theory and Practice of Computation

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 is to enable the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add values to business processes. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, deduplication, accuracy, 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 1 introduces data quality issues. A conditional dependency theory is developed in Chapter 2, for capturing data inconsistencies. It is followed by practical techniques in Chapter 3 for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter 4, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter 5, 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 6, 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 7. 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 proved, 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.

Wenfei Fan

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