This paper provides an overview of methods and systems developed for record linkage. Modern record linkage begins with the pioneering work of Newcombe and is especially based on the formal mathematical model of Fellegi and Sunter. In their seminal work, Fellegi and Sunter introduced many powerful ideas for estimating record linkage parameters and other ideas that still influence record linkage today. Record linkage research is characterized by its synergism of statistics, computer science, and operations research. Many difficult algorithms have been developed and put in software systems. Record linkage practice is still very limited. Some limits are due to existing software. Other limits are due to the difficulty in automatically estimating matching parameters and error rates, with current research highlighted by the work of Larsen and Rubin. Keywords: computer matching, modeling, iterative fitting, string comparison, optimization RsSUMs Cet article donne une vue d'ensemble sur les ...

INTRODUCTION Matching has a long history of uses in statistical surveys and administrative data development. A business register consisting of names, addresses, and other identifying information such as total financial receipts might be constructed from tax and employment data bases (see chapters by Colledge, Nijhowne, and Archer). A survey of retail establishments or agricultural establishments might combine results from an area frame and a list frame. To produce a combined estimator, units from the area frame would need to be identified in the list frame (see Vogel-Kott chapter). To estimate the size of a (sub)population via capture-recapture techniques, one needs to accurately determine units common to two or more independent listings (Sekar and Deming 1949; Scheuren 1983; Winkler 1989b). Samples must be drawn appropriately to estimate overlap (Deming and Gleser 1959). Rather than develop a special survey to collect data for policy decisions, it might be more appropriate t

Record linkage is the task of quickly and accurately identifying records corresponding to the same entity from one or more data sources. Record linkage is also known as data cleaning, entity reconciliation or identification and the merge/purge problem. This paper presents the "standard" probabilistic record linkage model and the associated algorithm. Recent work in information retrieval, federated database systems and data mining have proposed alternatives to key components of the standard algorithm. The impact of these alternatives on the standard approach are assessed. The key question is whether and how these new alternatives are better in terms of time, accuracy and degree of automation for a particular record linkage application.

Join techniques deploying approximate match predicates are fundamental data cleaning operations. A variety of predicates have been utilized to quantify approximate match in such operations and some have been embedded in a declarative data cleaning framework. These techniques return pairs of tuples from both relations, tagged with a score, signifying the degree of similarity between the tuples in the pair according to the specific approximate match predicate. In this paper we consider the problem of estimating various parameters on the output of declarative approximate join algorithms for planning purposes. Such algorithms are highly time consuming, so precise knowledge of the result size as well as its score distribution is a pressing concern. This knowledge aids decisions as to which operations are more promising for identifying highly similar tuples which is a key operation for data cleaning. We propose solution strategies that fully comply with a declarative framework and analytically reason about the quality of the estimates we obtain as well as the performance of our strategies. We present the results of a detailed performance evaluation of all strategies proposed. Our experimental results, validate our analytical expectations and shed additional light to the quality and performance of our estimation framework. Our study offers a set of simple, fully declarative techniques for this problem, which are readily deployed in the SPIDER declarative data cleaning system. 1

Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize data quality primitives on top of any relational data source. A primary advantage of such an approach is the ease of use and integration with existing applications. Over the last couple of years several similarity predicates have been proposed for common quality primitives (approximate selections, joins, etc.) and have been fully expressed using declarative SQL statements. In this thesis, new similarity predicates are proposed along with their declarative realization, based on notions of probabilistic information retrieval. Then, full declarative specifications of previously proposed similarity predicates in the literature are presented, grouped into classes according to their primary characteristics. Finally, a thorough performance and accuracy study comparing a large number of similarity predicates for data cleaning operations is performed. ii Dedication This thesis is dedicated to my brother, Aidin, and to my parents who have always supported me. iii Acknowledgements First, I would like to thank my supervisor, Nick Koudas. This work would not have been possible without his invaluable guidance and support. Special thanks to John Mylopoulos, the second reader of my thesis, for his valuable time and comments. During my research, I had the pleasure of working in a wonderful atmosphere in the database lab. I had an unforgettable year with my colleagues there. While enjoying the taste of fresh coffee from our fancy coffee machine that helped us stay awake all long nights before the deadlines, we had many fruitful discussions that often resulted in brilliant new ideas. I would like to thank all my friends in the database lab, particularly