PROPOLIS: Provisioned Analysis of Data-Centric Processes. - Daniel Deutch; Yuval Moskovitch; Val Tannen (Proc. VLDB Endow., 2013)

A Provenance Framework for Data-Dependent Process Analysis. - Daniel Deutch; Yuval Moskovitch; Val Tannen (Proc. VLDB Endow., 2014)

selP: Selective tracking and presentation of data provenance. - Daniel Deutch; Amir Gilad; Yuval Moskovitch (ICDE, 2015)

Towards web-scale how-provenance. - Daniel Deutch; Amir Gilad; Yuval Moskovitch (ICDE Workshops, 2015)

Selective Provenance for Datalog Programs Using Top-K Queries. - Daniel Deutch; Amir Gilad; Yuval Moskovitch (Proc. VLDB Endow., 2015)

Provenance-based analysis of data-centric processes. - Daniel Deutch; Yuval Moskovitch; Val Tannen (VLDB J., 2015)

Analyzing data-centric applications: Why, what-if, and how-to. - Pierre Bourhis; Daniel Deutch; Yuval Moskovitch (ICDE, 2016)

POLYTICS: Provenance-Based Analytics of Data-Centric Applications. - Pierre Bourhis; Daniel Deutch; Yuval Moskovitch (ICDE, 2017)

Towards Hypothetical Reasoning Using Distributed Provenance. - Daniel Deutch; Yuval Moskovitch; Itay Polak; Noam Rinetzky (EDBT, 2018)

Efficient provenance tracking for datalog using top-k queries. - Daniel Deutch; Amir Gilad; Yuval Moskovitch (VLDB J., 2018)

PODIUM: Probabilistic Datalog Analysis via Contribution Maximization. - Tova Milo; Yuval Moskovitch; Brit Youngmann (CIKM, 2019)

COBRA: Compression Via Abstraction of Provenance for Hypothetical Reasoning. - Daniel Deutch; Yuval Moskovitch; Noam Rinetzky (ICDE, 2019)

Hypothetical Reasoning via Provenance Abstraction. - Daniel Deutch; Yuval Moskovitch; Noam Rinetzky (SIGMOD Conference, 2019)

Datalignment: Ontology Schema Alignment Through Datalog Containment. - Daniel Deutch; Evgeny Marants; Yuval Moskovitch (Proc. VLDB Endow., 2019)

Provenance for Data-Centric Applications - Yuval Moskovitch (2019)

Towards Inferring Queries from Simple and Partial Provenance Examples. - Amir Gilad; Yuval Moskovitch (CIKM, 2020)

Contribution Maximization in Probabilistic Datalog. - Tova Milo; Yuval Moskovitch; Brit Youngmann (ICDE, 2020)

Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries. - Pierre Bourhis; Daniel Deutch; Yuval Moskovitch (SIGMOD Conference, 2020)

COUNTATA: Dataset Labeling Using Pattern Counts. - Yuval Moskovitch; H. V. Jagadish (Proc. VLDB Endow., 2020)

PITA: Privacy Through Provenance Abstraction. - Daniel Deutch; Ariel Frankenthal; Amir Gilad; Yuval Moskovitch (ICDE, 2021)

Patterns Count-Based Labels for Datasets. - Yuval Moskovitch; H. V. Jagadish (ICDE, 2021)

On Optimizing the Trade-off between Privacy and Utility in Data Provenance. - Daniel Deutch; Ariel Frankenthal; Amir Gilad; Yuval Moskovitch (SIGMOD Conference, 2021)

DENOUNCER: Detection of Unfairness in Classifiers. - Jinyang Li 008; Yuval Moskovitch; H. V. Jagadish (Proc. VLDB Endow., 2021)

On Detecting Cherry-picked Generalizations. - Yin Lin; Brit Youngmann; Yuval Moskovitch; H. V. Jagadish; Tova Milo (Proc. VLDB Endow., 2021)

Bias analysis and mitigation in data-driven tools using provenance. - Yuval Moskovitch; Jinyang Li 008; H. V. Jagadish (TaPP, 2022)

Reliability at multiple stages in a data analysis pipeline. - Yuval Moskovitch; H. V. Jagadish (Commun. ACM, 2022)

OREO: Detection of Cherry-picked Generalizations. - Yin Lin; Brit Youngmann; Yuval Moskovitch; H. V. Jagadish; Tova Milo (Proc. VLDB Endow., 2022)

Detection of Groups with Biased Representation in Ranking. - Jinyang Li 008; Yuval Moskovitch; H. V. Jagadish (ICDE, 2023)

On Explaining Confounding Bias. - Brit Youngmann; Michael J. Cafarella; Yuval Moskovitch; Babak Salimi (ICDE, 2023)

Dexer: Detecting and Explaining Biased Representation in Ranking. - Yuval Moskovitch; Jinyang Li 008; H. V. Jagadish (SIGMOD Conference Companion, 2023)

NEXUS: On Explaining Confounding Bias. - Brit Youngmann; Michael J. Cafarella; Yuval Moskovitch; Babak Salimi (SIGMOD Conference Companion, 2023)

Query Refinement for Diversity Constraint Satisfaction. - Jinyang Li 008; Yuval Moskovitch; Julia Stoyanovich; H. V. Jagadish (Proc. VLDB Endow., 2023)

ERICA: Query Refinement for Diversity Constraint Satisfaction. - Jinyang Li 008; Alon Silberstein; Yuval Moskovitch; Julia Stoyanovich; H. V. Jagadish (Proc. VLDB Endow., 2023)

Query Refinement for Diverse Top-k Selection. - Felix S. Campbell; Alon Silberstein; Julia Stoyanovich; Yuval Moskovitch (Proc. ACM Manag. Data, 2024)

Rodeo: Making Refinements for Diverse Top-k Queries. - Felix S. Campbell; Julia Stoyanovich; Yuval Moskovitch (Proc. VLDB Endow., 2024)

Locator: Local Stability for Rankings. - Felix S. Campbell; Yuval Moskovitch (SIGMOD Conference Companion, 2025)

OmniTune: A Universal Framework for Query Refinement via LLMs. - Eldar Hacohen; Yuval Moskovitch; Amit Somech (SIGMOD Conference Companion, 2025)