Yuval Moskovitch

According to our database, Yuval Moskovitch authored at least 34 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Query Refinement for Diverse Top-k Selection.

Felix S. Campbell, Alon Silberstein, Julia Stoyanovich, Yuval Moskovitch

CoRR, 2024

2023

ERICA: Query Refinement for Diversity Constraint Satisfaction.

Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish

Proc. VLDB Endow., 2023

Query Refinement for Diversity Constraint Satisfaction.

Jinyang Li, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish

Proc. VLDB Endow., 2023

NEXUS: On Explaining Confounding Bias.

Brit Youngmann, Michael J. Cafarella, Yuval Moskovitch, Babak Salimi

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

Dexer: Detecting and Explaining Biased Representation in Ranking.

Yuval Moskovitch, Jinyang Li, H. V. Jagadish

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

On Explaining Confounding Bias.

Brit Youngmann, Michael J. Cafarella, Yuval Moskovitch, Babak Salimi

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Detection of Groups with Biased Representation in Ranking.

Jinyang Li, Yuval Moskovitch, H. V. Jagadish

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

OREO: Detection of Cherry-picked Generalizations.

Yin Lin, Brit Youngmann, Yuval Moskovitch, H. V. Jagadish, Tova Milo

Proc. VLDB Endow., 2022

Reliability at multiple stages in a data analysis pipeline.

Yuval Moskovitch, H. V. Jagadish

Commun. ACM, 2022

Bias analysis and mitigation in data-driven tools using provenance.

Yuval Moskovitch, Jinyang Li, H. V. Jagadish

Proceedings of the 14th International Workshop on the Theory and Practice of Provenance, 2022

2021

On Detecting Cherry-picked Generalizations.

Yin Lin, Brit Youngmann, Yuval Moskovitch, H. V. Jagadish, Tova Milo

Proc. VLDB Endow., 2021

DENOUNCER: Detection of Unfairness in Classifiers.

Jinyang Li, Yuval Moskovitch, H. V. Jagadish

Proc. VLDB Endow., 2021

On Optimizing the Trade-off between Privacy and Utility in Data Provenance.

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Patterns Count-Based Labels for Datasets.

Yuval Moskovitch, H. V. Jagadish

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

PITA: Privacy Through Provenance Abstraction.

Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

2020

COUNTATA: Dataset Labeling Using Pattern Counts.

Yuval Moskovitch, H. V. Jagadish

Proc. VLDB Endow., 2020

Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries.

Pierre Bourhis, Daniel Deutch, Yuval Moskovitch

Proceedings of the 2020 International Conference on Management of Data, 2020

Contribution Maximization in Probabilistic Datalog.

Tova Milo, Yuval Moskovitch, Brit Youngmann

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

Towards Inferring Queries from Simple and Partial Provenance Examples.

Amir Gilad, Yuval Moskovitch

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019

Provenance for Data-Centric Applications

Yuval Moskovitch

PhD thesis, 2019

Datalignment: Ontology Schema Alignment Through Datalog Containment.

Daniel Deutch, Evgeny Marants, Yuval Moskovitch

Proc. VLDB Endow., 2019

Hypothetical Reasoning via Provenance Abstraction.

Daniel Deutch, Yuval Moskovitch, Noam Rinetzky

Proceedings of the 2019 International Conference on Management of Data, 2019

COBRA: Compression Via Abstraction of Provenance for Hypothetical Reasoning.

Daniel Deutch, Yuval Moskovitch, Noam Rinetzky

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

PODIUM: Probabilistic Datalog Analysis via Contribution Maximization.

Tova Milo, Yuval Moskovitch, Brit Youngmann

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

2018

Efficient provenance tracking for datalog using top-k queries.

Daniel Deutch, Amir Gilad, Yuval Moskovitch

VLDB J., 2018

Towards Hypothetical Reasoning Using Distributed Provenance.

Daniel Deutch, Yuval Moskovitch, Itay Polak, Noam Rinetzky

Proceedings of the 21st International Conference on Extending Database Technology, 2018

2017

POLYTICS: Provenance-Based Analytics of Data-Centric Applications.

Pierre Bourhis, Daniel Deutch, Yuval Moskovitch

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

2016

Analyzing data-centric applications: Why, what-if, and how-to.

Pierre Bourhis, Daniel Deutch, Yuval Moskovitch

Proceedings of the 32nd IEEE International Conference on Data Engineering, 2016

2015

Provenance-based analysis of data-centric processes.

Daniel Deutch, Yuval Moskovitch, Val Tannen

VLDB J., 2015

Selective Provenance for Datalog Programs Using Top-K Queries.

Daniel Deutch, Amir Gilad, Yuval Moskovitch

Proc. VLDB Endow., 2015

Towards web-scale how-provenance.

Daniel Deutch, Amir Gilad, Yuval Moskovitch

Proceedings of the 31st IEEE International Conference on Data Engineering Workshops, 2015

selP: Selective tracking and presentation of data provenance.

Daniel Deutch, Amir Gilad, Yuval Moskovitch

Proceedings of the 31st IEEE International Conference on Data Engineering, 2015

2014

A Provenance Framework for Data-Dependent Process Analysis.

Daniel Deutch, Yuval Moskovitch, Val Tannen

Proc. VLDB Endow., 2014

2013

PROPOLIS: Provisioned Analysis of Data-Centric Processes.

Daniel Deutch, Yuval Moskovitch, Val Tannen

Proc. VLDB Endow., 2013