1 citations · 2 across the 5 of their papers we have counts for
6 papers
Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu, Su Feng, Boris Glavic +1
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive…
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…
Computing expected multiplicities for bag-TIDBs with bounded multiplicities
Su Feng, Boris Glavic, Aaron Huber +2
In this work, we study the problem of computing a tuple's expected multiplicity over probabilistic databases with bag semantics (where each tuple is associated with a multiplicity)…
Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds (extended version)
Su Feng, Aaron Huber, Boris Glavic +1
Certain answers are a principled method for coping with the uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude…
Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain Answers (extended version)
Su Feng, Aaron Huber, Boris Glavic +1
Certain answers are a principled method for coping with uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude use…
Debugging Transactions and Tracking their Provenance with Reenactment
Xing Niu, Bahareh Sadat Arab, Seokki Lee +6
Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit t…