most citedExplaining Natural Language Query Results

17 citations · 31 across the 8 of their papers we have counts for

collaborators

8 papers

cs.DB2020

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…

cs.DB2020

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…

cs.DB2020

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…

cs.DB202017 cited

Explaining Natural Language Query Results

Daniel Deutch, Nave Frost, Amir Gilad

Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form…

cs.DB20204 cited

Just in Time: Personal Temporal Insights for Altering Model Decisions

Naama Boer, Daniel Deutch, Nave Frost +1

The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as…

cs.DB20202 cited

T-REx: Table Repair Explanations

Daniel Deutch, Nave Frost, Amir Gilad +1

Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has b…