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20122022
most citedComputing Local Sensitivities of Counting Queries with Joins

37 citations · 51 across the 9 of their papers we have counts for

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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.DB20212 cited

Putting Things into Context: Rich Explanations for Query Answers using Join Graphs (extended version)

Chenjie Li, Zhengjie Miao, Qitian Zeng +2

In many data analysis applications, there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have direct…

cs.DB2020

Aggregated Deletion Propagation for Counting Conjunctive Query Answers

Xiao Hu, Shouzhuo Sun, Shweta Patwa +2

We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. This is a varia…

cs.DB202037 cited

Computing Local Sensitivities of Counting Queries with Joins

Yuchao Tao, Xi He, Ashwin Machanavajjhala +1

Local sensitivity of a query Q given a database instance D, i.e. how much the output Q(D) changes when a tuple is added to D or deleted from D, has many applications including quer…

cs.DB20206 cited

Causal Relational Learning

Babak Salimi, Harsh Parikh, Moe Kayali +3

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in ca…