Felix Naumann – författare

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.

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. Despite more than a decade of intensive research in these areas, there remain many unsolved problems. In some respects, these problems are becoming more severe, both due to continuous increases in data volumes and the growing diversity in types of data that need to be managed. And the next big challenge is already upon us: the need to integrate the di?- ent “omics” data sets with the vast amounts of clinical data, collected daily in thousands of hospitals and physicians’ o?ces all over the world. DILS 2006 is the third in an annual workshop series that aims at fostering discussion, exchange, and innovation in research and development in the areas of data integration and data management for the life science. DILS 2004 in Leipzig and DILS 2005 in San Diego each attracted around 100 researchersfrom all over the world. This year the number of submitted papers again increased. The Program Committee selected 23 papers out of 50 strong full submissions.

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. Despite more than a decade of intensive research in these areas, there remain many unsolved problems. In some respects, these problems are becoming more severe, both due to continuous increases in data volumes and the growing diversity in types of data that need to be managed. And the next big challenge is already upon us: the need to integrate the di?- ent “omics” data sets with the vast amounts of clinical data, collected daily in thousands of hospitals and physicians’ o?ces all over the world. DILS 2006 is the third in an annual workshop series that aims at fostering discussion, exchange, and innovation in research and development in the areas of data integration and data management for the life science. DILS 2004 in Leipzig and DILS 2005 in San Diego each attracted around 100 researchersfrom all over the world. This year the number of submitted papers again increased. The Program Committee selected 23 papers out of 50 strong full submissions.

The Internet and the World Wide Web (WWW) are becoming more and more important in our highly interconnected world as more and more data and information is made available for online access. Many individuals and governmental, commercial, cultural, and scientific organizations increasingly depend on information sources that can be accessed and queried over the Web. For example, accessing flight schedules or retrieving stock information has become common practice in todays world. When accessing this data, many people assume that the information accessed is accurate and that the data source can be accessed reliably. These two examples clearly demonstrate that not only the information content is important, the information about the quality of the data becomes an even more crucial and critical aspect for individuals and organizations when they make plans or take decisions based on the results of their queries. More precisely, having access to information of known quality becomes critical for the well-being and indeed for the functioning of modern industrialized societies. Surprisingly, despite the urgent need for clear concepts and techniques to judge and value quality and for technology to use such (meta) information, very few scientific results are known and available. Few approaches are known to use quality measures for accessing and querying information over the Web. Only a limited number of products on the IT market address this burning problem.

The Internet and the World Wide Web (WWW) are becoming more and more important in our highly interconnected world as more and more data and information is made available for online access. Many individuals and governmental, commercial, cultural, and scientific organizations increasingly depend on information sources that can be accessed and queried over the Web. For example, accessing flight schedules or retrieving stock information has become common practice in todays world. When accessing this data, many people assume that the information accessed is accurate and that the data source can be accessed reliably. These two examples clearly demonstrate that not only the information content is important, the information about the quality of the data becomes an even more crucial and critical aspect for individuals and organizations when they make plans or take decisions based on the results of their queries. More precisely, having access to information of known quality becomes critical for the well-being and indeed for the functioning of modern industrialized societies. Surprisingly, despite the urgent need for clear concepts and techniques to judge and value quality and for technology to use such (meta) information, very few scientific results are known and available. Few approaches are known to use quality measures for accessing and querying information over the Web. Only a limited number of products on the IT market address this burning problem.

Dieses kleine Lehrbuch aus der Reihe „Was ist eigentlich …?“ gibt eine kurze Einführung in die grundlegenden Methoden der quantitativen Statistik in sozialwissenschaftlichen Fächern. Sie will Studierenden der Psychologie, Sozialwissenschaften oder verwandter Fächer zu Beginn des Studiums bei einer Orientierung in dieser Teildisziplin verhelfen. Sie bietet aber auch für Fachfremde, die an psychologischen Methoden interessiert sind, einen ersten Einblick in die deskriptive Statistik, Inferenzstatistik sowie statistische Modellierung.