2 citations · 3 across the 7 of their papers we have counts for
7 papers
On Explaining Confounding Bias
Brit Youngmann, Michael Cafarella, Yuval Moskovitch +1
When analyzing large datasets, analysts are often interested in the explanations for surprising or unexpected results produced by their queries. In this work, we focus on aggregate…
On Optimizing the Trade-off between Privacy and Utility in Data Provenance
Daniel Deutch, Ariel Frankenthal, Amir Gilad +1
Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed…
Patterns Count-Based Labels for Datasets
Yuval Moskovitch, H. V. Jagadish
Counts of attribute-value combinations are central to the profiling of a dataset, particularly in determining fitness for use and in eliminating bias and unfairness. While counts o…
Towards Inferring Queries from Simple and Partial Provenance Examples
Amir Gilad, Yuval Moskovitch
The field of query-by-example aims at inferring queries from output examples given by non-expert users, by finding the underlying logic that binds the examples. However, for a very…
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries
Pierre Bourhis, Daniel Deutch, Yuval Moskovitch
The algebraic approach for provenance tracking, originating in the semiring model of Green et. al, has proven useful as an abstract way of handling metadata. Commutative Semirings…
Hypothetical Reasoning via Provenance Abstraction
Daniel Deutch, Yuval Moskovitch, Noam Rinetzky
Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Previo…