The STRINGER Project      Project Description | People | Publications | Demo                 Project Description     STRINGER: Duplicate Detection System for String Data     String data is ubiquitous. Many important pieces of information such as object identifiers, people names and addresses, titles of documents and web adresses (URIs) are stored in the form of string data. However, string data is prone to different types of errors, including data entry errors (typos and misspellings) and differences in standards and notations. On the other hand, approximate duplicate detection in large data sources of string data is inherently a difficult task which often requires human labour and expertise. Although standard techniques can automatically detect several types of errors in data, in many cases fully automatic detection of duplicates may not be plausible. In certain cases even a human expert may not be able to identify duplicates. For example, it is relatively easy and straightforward to recognize that "Schwarzenegear" is a misspeling of "Schwarzenegger" and they both refer to the same entity, but it is not easy to judge whether "STRANGER project" is a misspeling of "STRINGER project" or is a new item referring to a different entity. This project deals with string data and many important challenges raised in data sources with strings like the one stated above. The goal of this project is to provide accurate yet efficient automatic and semi-automatic techniques for approximate string matching in large data sources.      People   Project Members Oktie Hassanzadeh  (PhD Student, University of Toronto) Renée J. Miller  (Professor, University of Toronto) Collaborators Fei Chiang  (PhD Student, University of Toronto) Hyun Chul Lee  (PostDoc, University of Toronto Mohammad Sadoghi  (MSc Student, University of Toronto)      Publications   Framework For Evaluating Clustering Algorithms In Duplicate Detection O. Hassanzadeh, F. Chiang, H. C. Lee, and R. J. Miller. Proceedings of the VLDB Endowment VLDB Endowment, Volume 2, Issue 1, August 2009 Creating Probabilistic Databases from Duplicated Data  O. Hassanzadeh and R. J. Miller. The VLDB Journal. Volume 18, Issue 5, October 2009 Accuracy of Approximate String Joins O. Hassanzadeh, M. Sadoghi and R. J. Miller. QDB'07 at VLDB. Vienna, Austria Probabilistic Management Of Duplicated Data O. Hassanzadeh and Renée J. Miller. Technical Report CSRG-568      Related Projects   ConQuer: Efficient Management of Inconsistent Databases  LinQuer: Linkage Query Writer      Datasets and Experimental Results   Datasets and Experimental Results Clustering Algorithms Evaluation Results      Demo

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