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cs.DB2023
Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data (Extended version)
Su Feng, Boris Glavic, Oliver Kennedy
Uncertainty arises naturally inmany application domains due to, e.g., data entry errors and ambiguity in data cleaning. Prior work in incomplete and probabilistic databases has inv…
cs.DB2017★ 4 cited
Efficiently Computing Provenance Graphs for Queries with Negation
Seokki Lee, Sven Koehler, Bertram Ludaescher +1
Explaining why an answer is in the result of a query or why it is missing from the result is important for many applications including auditing, debugging data and queries, and ans…
cs.DB2017★ 2 cited
Optimizing Provenance Computations
Xing Niu, Boris Glavic
Data provenance is essential for debugging query results, auditing data in cloud environments, and explaining outputs of Big Data analytics. A well-established technique is to repr…