SQuID uses probabilistic abduction on a precomputed abduction-ready database to infer select-project-join queries with optional group-by and intersection from user examples, outperforming prior QBE and ML methods on real datasets.
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A 2-means clustering step cleans labels to enable logistic regression for positive-unlabeled classification without requiring the SCAR assumption.
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Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity
SQuID uses probabilistic abduction on a precomputed abduction-ready database to infer select-project-join queries with optional group-by and intersection from user examples, outperforming prior QBE and ML methods on real datasets.
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A proposal for PU classification under Non-SCAR using clustering and logistic model
A 2-means clustering step cleans labels to enable logistic regression for positive-unlabeled classification without requiring the SCAR assumption.