This book constitutes the refereed proceedings of the Third International Workshop on Data Integration in the Life Sciences, DILS 2006, held in Hinxton, UK in July 2006. Presents 19 revised full papers and 4 revised short papers together with 2 keynote talks, addressing current issues in data integration from the life science point of view. The papers are organized in topical sections on data integration, text mining, systems, and workflow.

The Internet and the World Wide Web are becoming increasingly important in our highly interconnected world. This book addresses the topic of querying the data available, with regard to its quality, in a systematic and comprehensive way, from a database point of view. First, information quality and information quality measures are systematically introduced before ranking algorithms are developed for selecting Web sources for access. The second part is devoted to quality-driven query answering, particularly to query planning methods and algorithms.The in-depth presentation of algorithms and techniques for quality-oriented querying will serve as a valuable source of reference for R&D professionals and for IT business people. In addition, the work will provide students with a comprehensible introduction to cutting-edge research.

Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.