Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
A distributed framework for scaling Up LSH-based computations in privacy preserving record linkage
Proceedings of the 6th Balkan Conference in Informatics
Disinformation techniques for entity resolution
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Discovering linkage points over web data
Proceedings of the VLDB Endowment
Active learning of expressive linkage rules using genetic programming
Web Semantics: Science, Services and Agents on the World Wide Web
Toward detection of aliases without string similarity
Information Sciences: an International Journal
Incremental entity resolution on rules and data
The VLDB Journal — The International Journal on Very Large Data Bases
Joint entity resolution on multiple datasets
The VLDB Journal — The International Journal on Very Large Data Bases
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Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christens book is divided into three parts: Part I, Overview, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, Steps of the Data Matching Process, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, Further Topics, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.