Peter Christen – författare

The first part introduces the reader to the topic of linking sensitive data, the second part coversmethods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases.

The first part introduces the reader to the topic of linking sensitive data, the second part coversmethods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases.

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second  International Workshop  on  eXplainable  Knowledge  Discovery in  Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.

This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major phases, reflected in the three main sections of this book. The first phase involves transcribing and digitizing the data while structuring the information in a meaningful and efficient way. In the second phase, records that refer to the same person or group of persons are identified by a process of linkage. In the third and final phase, the information on an individual is combined into a reconstruction of their life course.The studies and examples in this book originate from a range of countries, each with its own cultural and administrative characteristics, and from medieval charters through historical censuses and vital registration, to the modern issue of privacy preservation. Despite the diverse places and times addressed, they all share the study of fundamental issues when it comes to model reasoning for population reconstruction and the possibilities and limitations of information technology to support this process.It is thus not a single discipline that is involved in such an endeavor. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources, and the possible interpretations of information. The availability of big data from digitized archives and the need for complex analyses to identify individuals calls for the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of the digital humanities, and will hopefully offer a source of inspiration for future investigations.

This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major phases, reflected in the three main sections of this book. The first phase involves transcribing and digitizing the data while structuring the information in a meaningful and efficient way. In the second phase, records that refer to the same person or group of persons are identified by a process of linkage. In the third and final phase, the information on an individual is combined into a reconstruction of their life course.The studies and examples in this book originate from a range of countries, each with its own cultural and administrative characteristics, and from medieval charters through historical censuses and vital registration, to the modern issue of privacy preservation. Despite the diverse places and times addressed, they all share the study of fundamental issues when it comes to model reasoning for population reconstruction and the possibilities and limitations of information technology to support this process.It is thus not a single discipline that is involved in such an endeavor. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources, and the possible interpretations of information. The availability of big data from digitized archives and the need for complex analyses to identify individuals calls for the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of the digital humanities, and will hopefully offer a source of inspiration for future investigations.

Five high-quality workshops were held at the 13th Paci?c-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Paci?c Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009),the workshopon Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD).It provides an internationalforum for researchersand industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visualization, causal induction and knowledge-based systems. In general,we wish to thank our General WorkshopCo-chairs,Manabu O- mura and Bernhard Pfahringe, for selecting and coordinating the great wo- shops. WewouldliketothankJunbinGao(CharlesSturtUniversity),PaulKwan (UniversityofNewEngland,Australia),JosiahPoon(UniversityofSydney),and Simon Poon (University of Sydney), for their arrangement of AIBDM 2009.

This inaugural volume on a topic of increasing importance collates research on databases, statistics, information retrieval, data mining and machine learning to provide a detailed discussion of the practical aspects and limitations of data matching.

This inaugural volume on a topic of increasing importance collates research on databases, statistics, information retrieval, data mining and machine learning to provide a detailed discussion of the practical aspects and limitations of data matching.