activity
20202022
most citedExplaining Natural Language Query Results

17 citations · 27 across the 7 of their papers we have counts for

collaborators

9 papers

cs.DB20225 cited

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…

cs.DB20222 cited

HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach

Sainyam Galhotra, Amir Gilad, Sudeepa Roy +1

What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical sce…

cs.DB2022

Understanding Queries by Conditional Instances

Amir Gilad, Zhengjie Miao, Sudeepa Roy +1

A powerful way to understand a complex query is by observing how it operates on data instances. However, specific database instances are not ideal for such observations: they often…

cs.SI2021

Heterogeneous Treatment Effects in Social Networks

Amir Gilad, Harsh Parikh, Sudeepa Roy +1

We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is…

cs.DB2021

Synthesizing Linked Data Under Cardinality and Integrity Constraints

Amir Gilad, Shweta Patwa, Ashwin Machanavajjhala

The generation of synthetic data is useful in multiple aspects, from testing applications to benchmarking to privacy preservation. Generating the links between relations, subject t…

cs.DB2021

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…