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个人简介
My main research interest is in databases. In particular, I am interested in query processing over:
Probabilistic, Inconsistent, and Uncertain Databases
In real applications such as location-based services (LBS), RFID/sensor networks, data extraction/integration, and medical data analysis, the underlying data are inherently imprecise and uncertain, due to various reasons such as imperfect nature of sensing devices, inaccuracy of information extraction methods, unreliability of data sources, and/or privacy preserving. Therefore, these application data can be modeled by probabilistic and uncertain data. Compared with certain data, uncertain data are those tuples/objects associated with probabilistic confidences that are either independent or with arbitrary correlations. Moreover, in some applications such as data extraction/integration, the extracted/integrated (probabilistic) data may violate some integrity constraints (e.g., functional dependencies) and thus be inconsistent. Due to the data uncertainty and inconsistency, it is challenging to efficiently and accurately organize and answer various probabilistic queries over such probabilistic/inconsistent/uncertain data.
Uncertain and Certain Graph Databases
Uncertain and certain graph databases have been widely used in many real applications such as the Semantic Web (e.g., workflows and XML/RDF graphs), social networks, scientific databases (e.g., chemical compound databases, biological graphs like protein-to-protein interaction networks and gene regulatory networks, etc.), and transportation systems (e.g., road networks). There are many interesting research topics on efficient query answering in uncertain/certain graph databases, such as keyword search queries over (probabilistic) RDF graphs, route planning over road networks with uncertain traffic conditions, and variants of subgraph matching over (probabilistic) RDF graphs, biological graphs, or social networks. While the manipulation over complex graph structures itself is quite costly, the query processing over probabilistic graphs is more challenging (since more constraints such as labels (keywords), probabilities, and correlations are involved)
Probabilistic, Inconsistent, and Uncertain Databases
In real applications such as location-based services (LBS), RFID/sensor networks, data extraction/integration, and medical data analysis, the underlying data are inherently imprecise and uncertain, due to various reasons such as imperfect nature of sensing devices, inaccuracy of information extraction methods, unreliability of data sources, and/or privacy preserving. Therefore, these application data can be modeled by probabilistic and uncertain data. Compared with certain data, uncertain data are those tuples/objects associated with probabilistic confidences that are either independent or with arbitrary correlations. Moreover, in some applications such as data extraction/integration, the extracted/integrated (probabilistic) data may violate some integrity constraints (e.g., functional dependencies) and thus be inconsistent. Due to the data uncertainty and inconsistency, it is challenging to efficiently and accurately organize and answer various probabilistic queries over such probabilistic/inconsistent/uncertain data.
Uncertain and Certain Graph Databases
Uncertain and certain graph databases have been widely used in many real applications such as the Semantic Web (e.g., workflows and XML/RDF graphs), social networks, scientific databases (e.g., chemical compound databases, biological graphs like protein-to-protein interaction networks and gene regulatory networks, etc.), and transportation systems (e.g., road networks). There are many interesting research topics on efficient query answering in uncertain/certain graph databases, such as keyword search queries over (probabilistic) RDF graphs, route planning over road networks with uncertain traffic conditions, and variants of subgraph matching over (probabilistic) RDF graphs, biological graphs, or social networks. While the manipulation over complex graph structures itself is quite costly, the query processing over probabilistic graphs is more challenging (since more constraints such as labels (keywords), probabilities, and correlations are involved)
研究兴趣
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arxiv(2024)
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Spatial Information Researchno. 2 (2024): 195-206
Proc. ACM Manag. Datano. 1 (2024): 29:1-29:25
Proceedings of the VLDB Endowmentno. 7 (2023): 1628-1641
CoRR (2023)
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Knowl. Inf. Syst.no. 11 (2023): 4939-4965
Proc. VLDB Endow.no. 11 (2023): 2818-2831
ICDEpp.3787-3788, (2023)
Parallel and Distributed Processing with Applicationspp.56-63, (2023)
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