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About Me
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Research Interests
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I'm interested in effective and efficient handling of text in databases.
With the advance of technologies, a vast amount of text data are generated by users such as blogs, comments, twits and profiles.
Such data are hardly error-free with typos and different spelling conventions.
They may also be collected from multiple sources and lack standardized representations.
These changes call for approximate or error tolerant query processing in databases and
there have been growing interests in this direction. My research has been focusing on developing size estimation techniques
for approximate text queries, which is crucial in optimizing such queries.
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Publications
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Hongrae Lee, Raymond Ng, Kyuseok Shim.
Power-Law Based Estimation of Set Similarity Join Size.
To appear In Proceedings of 35th International Conference on Very Large Data Bases (VLDB),
Lyon, France, 2009.
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Hongrae Lee, Raymond Ng, Kyuseok Shim.
Approximate Substring Selectivity Estimation.
In Proceedings of 12th International Conference on Extending Database Technology (EDBT),
Saint-Petersburg, Russia, 2009.
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Hongrae Lee, Raymond Ng, Kyuseok Shim.
Extending Q-Grams to Estimate Selectivity of String Matching with Low Edit Distance.
In Proceedings of 33rd International Conference on Very Large Data Bases (VLDB),
pages 195-206, Vienna, Austria, 2007.
[pdf]
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Hongrae Lee, Kyuseok Shim, Hyoung-Joo Kim. Compact Suffix Graph for
Substring Selectivity Estimation. Journal of KISS, 34(2), Apr. 2007 (In Korean)
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Awards and Honors
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