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• W4384659457 abstract "There has been a growing interest in cross-source searching to gain rich knowledge in recent years. A data lake collects massive raw and heterogeneous data with different data schemas and query interfaces. Many real-life applications require query answering over the heterogeneous data lake, such as e-commerce, bioinformatics and healthcare. In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. To this end, we propose a novel framework to efficiently and effectively perform the cross-source searching. The framework exploits a reinforcement learning method to semantically integrate the data schemas and further create a global relational schema for the heterogeneous data. It then performs a query answering algorithm based on the global schema to find answers across multiple data sources. We conduct extensive experimental evaluations using real-life data to verify that our approach outperforms existing solutions in terms of effectiveness and efficiency." @default.
• W4384659457 created "2023-07-20" @default.
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• W4384659457 date "2023-07-18" @default.
• W4384659457 modified "2023-10-16" @default.
• W4384659457 title "An Effective Framework for Enhancing Query Answering in a Heterogeneous Data Lake" @default.
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• W4384659457 doi "https://doi.org/10.1145/3539618.3591637" @default.
• W4384659457 hasPublicationYear "2023" @default.
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