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Seung-won Hwang
Researcher at Yonsei University
Publications - 191
Citations - 3363
Seung-won Hwang is an academic researcher from Yonsei University. The author has contributed to research in topics: Skyline & Ranking (information retrieval). The author has an hindex of 29, co-authored 183 publications receiving 3007 citations. Previous affiliations of Seung-won Hwang include Microsoft & Pohang University of Science and Technology.
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Proceedings ArticleDOI
Minimal probing: supporting expensive predicates for top-k queries
TL;DR: Algorithm MPro is proposed which, by implementing the formal principle of "necessary probes," is provably optimal with minimal probe cost, and experiments show that MPro enables significant probe reduction, which can be orders of magnitude faster than the standard scheme using complete probing.
Proceedings ArticleDOI
Automatic categorization of query results
TL;DR: This paper dynamically generates a labeled, hierarchical category structure that users can determine whether a category is relevant or not by examining simply its label; she can then explore just the relevant categories and ignore the remaining ones, thereby reducing information overload.
Journal ArticleDOI
Enriching Documents with Examples: A Corpus Mining Approach
TL;DR: This work proposes a novel code example recommendation system that combines the strength of browsing documents and searching for code examples and returns API documents embedded with high-quality code example summaries mined from the Web.
Journal ArticleDOI
Personalized top-k skyline queries in high-dimensional space
TL;DR: This paper abstracts personalized skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference, and develops a novel algorithm navigating on a compressed structure itself, to reduce the storage overhead.
Proceedings Article
COSTRIAGE: a cost-aware triage algorithm for bug reporting systems
TL;DR: A topic-model is developed to reduce the sparseness and enhance the quality of CBCF, a content-boosted collaborative filtering (CBCF), combining an existing CBR with a collaborative filtering recommender (CF), which enhances the recommendation quality of either approach alone.