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个人简介
RESEARCH INTERESTS
My main research interest is data management. My contributions include new concepts and frameworks for the control of entity and referential integrity in databases, database design on the logical and physical level, data cleaning, data mining, and data profiling. This research applies to different models of data, such as relational databases, databases with missing information, SQL and Web databases, probabilistic and possibilistic databases. More specifically, I have introduced notions such as possible and certain keys, embedded uniqueness constraints, probabilistic keys, possibilistic keys, keys for property graphs, possible and certain functional functional dependencies, embedded functional dependencies, NOT NULL inclusion dependencies, keys and functional dependencies for XML, as well as multivalued and hierarchical dependencies for various data models, and have established axiomatic and low-degree polynomial algorithmic characterizations for their associated implication problem. I have extensively worked on structural and computational properties of perfect samples (Armstrong databases) for these and other classes of data dependencies. I have also developed various algorithms for the discovery problems associated with some of these classes. For example, Ziheng Wei and I established a new state-of-the-art algorithm for the discovery problem of functional dependencies. Recently, I have established various database schema design frameworks, including SQL, data-completeness tailored database design, and schema design for applications with uncertain data. For the relational model of data, I have introduced the Bounded Cardinality Normal Form, which quantifies the trade-off between data redundancy and join efficiency on the logical schema design level. Similarly, I have introduced the Composite Object Normal Form which measures the trade-off between access variety and update complexity by the number of minimal keys that a schema in Boyce-Codd Normal Form exhibits. I have also helped introduce the concept of non-invasive data cleansing.
研究兴趣
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ICDEpp.3853-3854, (2023)
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Inf. Syst. (2023): 102224
ACM J. Data Inf. Qual.no. 2 (2023): 13:1-13:29
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arxiv(2023)
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Inf. Syst. (2023): 102208-102208
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Proceedings of the ACM on Management of Datano. 1 (2023): 1-25
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Proc. VLDB Endow.no. 11 (2023): 3031-3043
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International Conference on Scalable Uncertainty Management (SUM)pp.351-360, (2022)
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