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个人简介
His teaching covered a quite range of courses at different leveles. He also supervised many graduate students. Xuemin Lin's principal research areas are databases and graph visualisation .
In databases, his research areas include data mining, data streams, distributed database systems, spatial database systems, and web databases. Currently, he is working on data streams,, approximate query processing, spatial data processing , probabilistic queries, Graph Databases , and web information processing.
Graph visualisation is to display a graph according to the aesthetics from applications; for example, one may wish to draw a graph symmetrically if possible. Graph visualisation serves as a tool in many applications. A number of topics have been covered by Xuemin in graph drawing, which include drawing trees, undirected graphs, directed graphs, and hierarchical graphs. Xuemin's main focus has been on understanding inherent difficulties of drawing problems, and then developing effective drawing algorithms.
In databases, his research areas include data mining, data streams, distributed database systems, spatial database systems, and web databases. Currently, he is working on data streams,, approximate query processing, spatial data processing , probabilistic queries, Graph Databases , and web information processing.
Graph visualisation is to display a graph according to the aesthetics from applications; for example, one may wish to draw a graph symmetrically if possible. Graph visualisation serves as a tool in many applications. A number of topics have been covered by Xuemin in graph drawing, which include drawing trees, undirected graphs, directed graphs, and hierarchical graphs. Xuemin's main focus has been on understanding inherent difficulties of drawing problems, and then developing effective drawing algorithms.
研究兴趣
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IEEE Transactions on Knowledge and Data Engineeringno. 99 (2024): 1-14
World Wide Webno. 2 (2024): 1-24
CoRR (2024)
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGno. 1 (2024): 356-371
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGno. 1 (2024): 281-295
Proceedings of the ACM on Management of Datano. 1 (2024): 1-29
IEEE Transactions on Knowledge and Data Engineeringno. 99 (2024): 1-14
arxiv(2024)
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ICDEpp.1313-1325, (2023)
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IEEE Transactions on Knowledge and Data Engineeringno. 12 (2023): 12513-12527
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