17 citations · 36 across the 11 of their papers we have counts for
10 papers · 1 filter
FEDEX: An Explainability Framework for Data Exploration Steps
Daniel Deutch, Amir Gilad, Tova Milo +2
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this pa…
Computing the Shapley Value of Facts in Query Answering
Daniel Deutch, Nave Frost, Benny Kimelfeld +1
The Shapley value is a game-theoretic notion for wealth distribution that is nowadays extensively used to explain complex data-intensive computation, for instance, in network analy…
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…
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…
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning
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. Recent…