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ORCIDYuval Moskovitch
Person information
- affiliation: Ben-Gurion University of the Negev, Department of Computer Science, Beersheba, Israel
- affiliation (former): University of Michigan, Ann Arbor, MI, USA
- affiliation (former): Tel Aviv University, Israel
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2020 – today
- 2024
[j15]Felix S. Campbell
, Alon Silberstein, Julia Stoyanovich, Yuval Moskovitch:
Query Refinement for Diverse Top-k Selection. Proc. ACM Manag. Data 2(3): 166 (2024)- [j14]Felix S. Campbell, Julia Stoyanovich, Yuval Moskovitch:
Rodeo: Making Refinements for Diverse Top-k Queries. Proc. VLDB Endow. 17(12): 4341-4344 (2024) - [i9]Felix S. Campbell, Alon Silberstein, Julia Stoyanovich, Yuval Moskovitch:
Query Refinement for Diverse Top-k Selection. CoRR abs/2403.17786 (2024) - 2023
- [j13]Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish:
ERICA: Query Refinement for Diversity Constraint Satisfaction. Proc. VLDB Endow. 16(12): 4070-4073 (2023) - [j12]Jinyang Li, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish:
Query Refinement for Diversity Constraint Satisfaction. Proc. VLDB Endow. 17(2): 106-118 (2023) - [c19]Brit Youngmann, Michael J. Cafarella, Yuval Moskovitch, Babak Salimi:
On Explaining Confounding Bias. ICDE 2023: 1846-1859 - [c18]Jinyang Li, Yuval Moskovitch, H. V. Jagadish:
Detection of Groups with Biased Representation in Ranking. ICDE 2023: 2167-2179 - [c17]Yuval Moskovitch, Jinyang Li, H. V. Jagadish:
Dexer: Detecting and Explaining Biased Representation in Ranking. SIGMOD Conference Companion 2023: 159-162 - [c16]Brit Youngmann, Michael J. Cafarella, Yuval Moskovitch, Babak Salimi:
NEXUS: On Explaining Confounding Bias. SIGMOD Conference Companion 2023: 171-174 - [i8]Yuval Moskovitch, Jinyang Li, H. V. Jagadish:
Detection of Groups with Biased Representation in Ranking. CoRR abs/2301.00719 (2023) - 2022
- [j11]Yuval Moskovitch, H. V. Jagadish:
Reliability at multiple stages in a data analysis pipeline. Commun. ACM 65(11): 118-128 (2022) - [j10]Yin Lin, Brit Youngmann, Yuval Moskovitch, H. V. Jagadish, Tova Milo:
OREO: Detection of Cherry-picked Generalizations. Proc. VLDB Endow. 15(12): 3570-3573 (2022) - [c15]Yuval Moskovitch, Jinyang Li, H. V. Jagadish:
Bias analysis and mitigation in data-driven tools using provenance. TaPP 2022: 1:1-1:4 - [i7]Brit Youngmann, Michael J. Cafarella, Yuval Moskovitch, Babak Salimi:
On Explaining Confounding Bias. CoRR abs/2210.02943 (2022) - 2021
- [j9]Jinyang Li, Yuval Moskovitch, H. V. Jagadish:
DENOUNCER: Detection of Unfairness in Classifiers. Proc. VLDB Endow. 14(12): 2719-2722 (2021) - [j8]Yin Lin, Brit Youngmann, Yuval Moskovitch, H. V. Jagadish, Tova Milo:
On Detecting Cherry-picked Generalizations. Proc. VLDB Endow. 15(1): 59-71 (2021) - [c14]Yuval Moskovitch, H. V. Jagadish:
Patterns Count-Based Labels for Datasets. ICDE 2021: 1961-1966 - [c13]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
PITA: Privacy Through Provenance Abstraction. ICDE 2021: 2713-2716 - [c12]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
On Optimizing the Trade-off between Privacy and Utility in Data Provenance. SIGMOD Conference 2021: 379-391 - [i6]Daniel Deutch, Ariel Frankenthal, Amir Gilad, Yuval Moskovitch:
On Optimizing the Trade-off between Privacy and Utility in Data Provenance. CoRR abs/2103.00288 (2021) - 2020
- [j7]Yuval Moskovitch, H. V. Jagadish:
COUNTATA: Dataset Labeling Using Pattern Counts. Proc. VLDB Endow. 13(12): 2829-2832 (2020) - [c11]Amir Gilad, Yuval Moskovitch:
Towards Inferring Queries from Simple and Partial Provenance Examples. CIKM 2020: 3273-3276 - [c10]Tova Milo, Yuval Moskovitch, Brit Youngmann:
Contribution Maximization in Probabilistic Datalog. ICDE 2020: 817-828 - [c9]Pierre Bourhis, Daniel Deutch, Yuval Moskovitch:
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries. SIGMOD Conference 2020: 415-429 - [i5]Daniel Deutch, Yuval Moskovitch, Noam Rinetzky:
COBRA: Compression via Abstraction of Provenance for Hypothetical Reasoning. CoRR abs/2007.05389 (2020) - [i4]Daniel Deutch, Yuval Moskovitch, Noam Rinetzky:
Hypothetical Reasoning via Provenance Abstraction. CoRR abs/2007.05400 (2020) - [i3]Pierre Bourhis, Daniel Deutch, Yuval Moskovitch:
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries. CoRR abs/2007.05463 (2020) - [i2]Amir Gilad, Yuval Moskovitch:
Towards Inferring Queries from Simple and Partial Provenance Examples. CoRR abs/2008.08989 (2020) - [i1]Yuval Moskovitch, H. V. Jagadish:
Patterns Count-Based Labels for Datasets. CoRR abs/2010.16340 (2020)
2010 – 2019
- 2019
- [b1]Yuval Moskovitch:
Provenance for Data-Centric Applications. Tel Aviv University, Israel, 2019 - [j6]Daniel Deutch, Evgeny Marants, Yuval Moskovitch:
Datalignment: Ontology Schema Alignment Through Datalog Containment. Proc. VLDB Endow. 12(12): 1870-1873 (2019) - [c8]Tova Milo, Yuval Moskovitch, Brit Youngmann:
PODIUM: Probabilistic Datalog Analysis via Contribution Maximization. CIKM 2019: 2865-2868 - [c7]Daniel Deutch, Yuval Moskovitch, Noam Rinetzky:
COBRA: Compression Via Abstraction of Provenance for Hypothetical Reasoning. ICDE 2019: 2016-2019 - [c6]Daniel Deutch, Yuval Moskovitch, Noam Rinetzky:
Hypothetical Reasoning via Provenance Abstraction. SIGMOD Conference 2019: 537-554 - 2018
- [j5]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
Efficient provenance tracking for datalog using top-k queries. VLDB J. 27(2): 245-269 (2018) - [c5]Daniel Deutch, Yuval Moskovitch, Itay Polak, Noam Rinetzky:
Towards Hypothetical Reasoning Using Distributed Provenance. EDBT 2018: 461-464 - 2017
- [c4]Pierre Bourhis, Daniel Deutch, Yuval Moskovitch:
POLYTICS: Provenance-Based Analytics of Data-Centric Applications. ICDE 2017: 1373-1374 - 2016
- [c3]Pierre Bourhis, Daniel Deutch, Yuval Moskovitch:
Analyzing data-centric applications: Why, what-if, and how-to. ICDE 2016: 779-790 - 2015
- [j4]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
Selective Provenance for Datalog Programs Using Top-K Queries. Proc. VLDB Endow. 8(12): 1394-1405 (2015) - [j3]Daniel Deutch, Yuval Moskovitch, Val Tannen:
Provenance-based analysis of data-centric processes. VLDB J. 24(4): 583-607 (2015) - [c2]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
Towards web-scale how-provenance. ICDE Workshops 2015: 68-70 - [c1]Daniel Deutch, Amir Gilad, Yuval Moskovitch:
selP: Selective tracking and presentation of data provenance. ICDE 2015: 1484-1487 - 2014
- [j2]Daniel Deutch, Yuval Moskovitch, Val Tannen:
A Provenance Framework for Data-Dependent Process Analysis. Proc. VLDB Endow. 7(6): 457-468 (2014) - 2013
- [j1]Daniel Deutch, Yuval Moskovitch, Val Tannen:
PROPOLIS: Provisioned Analysis of Data-Centric Processes. Proc. VLDB Endow. 6(12): 1302-1305 (2013)
Coauthor Index
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