37 citations · 149 across the 26 of their papers we have counts for
21 papers · 1 filter
DPXPlain: Privately Explaining Aggregate Query Answers
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala +1
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in t…
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…
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…
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…
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…
On Multiple Semantics for Declarative Database Repairs
Amir Gilad, Daniel Deutch, Sudeepa Roy
We study the problem of database repairs through a rule-based framework that we refer to as Delta Rules. Delta Rules are highly expressive and allow specifying complex, cross-relat…
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…
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…
Generalized Deletion Propagation on Counting Conjunctive Query Answers
Debmalya Panigrahi, Shweta Patwa, Sudeepa Roy
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. In particular,…
Learning to Sample: Counting with Complex Queries
Brett Walenz, Stavros Sintos, Sudeepa Roy +1
We study the problem of efficiently estimating counts for queries involving complex filters, such as user-defined functions, or predicates involving self-joins and correlated subqu…
Explaining Wrong Queries Using Small Examples
Zhengjie Miao, Sudeepa Roy, Jun Yang
For testing the correctness of SQL queries, e.g., evaluating student submissions in a database course, a standard practice is to execute the query in question on some test database…
Properties of Inconsistency Measures for Databases
Ester Livshits, Rina Kochirgan, Segev Tsur +3
How should we quantify the inconsistency of a database that violates integrity constraints? Proper measures are important for various tasks, such as progress indication and action…
Interactive Summarization and Exploration of Top Aggregate Query Answers
Yuhao Wen, Xiaodan Zhu, Sudeepa Roy +1
We present a system for summarization and interactive exploration of high-valued aggregate query answers to make a large set of possible answers more informative to the user. Our s…
Computing Optimal Repairs for Functional Dependencies
Ester Livshits, Benny Kimelfeld, Sudeepa Roy
We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two…
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
Sudeepa Roy, Babak Salimi
The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of va…
Answering Conjunctive Queries with Inequalities
Paraschos Koutris, Tova Milo, Sudeepa Roy +1
In this paper, we study the complexity of answering conjunctive queries (CQ) with inequalities). In particular, we are interested in comparing the complexity of the query with and…
Model Counting of Query Expressions: Limitations of Propositional Methods
Paul Beame, Jerry Li, Sudeepa Roy +1
Query evaluation in tuple-independent probabilistic databases is the problem of computing the probability of an answer to a query given independent probabilities of the individual…
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
Paul Beame, Jerry Li, Sudeepa Roy +1
The best current methods for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas can be seen, either directly or indirectly,…
A Propagation Model for Provenance Views of Public/Private Workflows
Susan B. Davidson, Tova Milo, Sudeepa Roy
We study the problem of concealing functionality of a proprietary or private module when provenance information is shown over repeated executions of a workflow which contains both…
Faster Query Answering in Probabilistic Databases using Read-Once Functions
Sudeepa Roy, Vittorio Perduca, Val Tannen
A boolean expression is in read-once form if each of its variables appears exactly once. When the variables denote independent events in a probability space, the probability of the…
Provenance Views for Module Privacy
Susan B. Davidson, Sanjeev Khanna, Tova Milo +2
Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate da…