Moe Kayali - RankMe

17 papers A 1Journal 14Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Mind the Data Gap: Bridging Large Language Models (LLMs) to Enterprise Data Integration. CIDR Moe Kayali, Fabian Wenz, Nesime Tatbul, Çagatay Demiralp
2025	—	`conf`	Reproducibility Report for ACM SIGMOD 2025 Paper: 'BCviz: A Linear-Space Index for Mining and Visualizing Cohesive Bipartite Subgraphs'. SIGMOD ARI Reports Moe Kayali, Jianxiong Ye
2025	J	`jnl`	SSH-Passkeys: Leveraging Web Authentication for Passwordless SSH. CoRR Moe Kayali, Jonas Schmitt, Franziska Roesner
2024	J	`jnl`	BEAVER: An Enterprise Benchmark for Text-to-SQL. CoRR Peter Baile Chen, Fabian Wenz, Yi Zhang, Moe Kayali, Nesime Tatbul, Michael J. Cafarella, Çagatay Demiralp, Michael Stonebraker
2024	J	`jnl`	CHORUS: Foundation Models for Unified Data Discovery and Exploration. Proc. VLDB Endow. Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, Dan Suciu
2024	J	`jnl`	COLOR: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation. Proc. VLDB Endow. Kyle B. Deeds, Diandre Sabale, Moe Kayali, Dan Suciu
2024	J	`jnl`	Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation. CoRR Kyle Deeds, Diandre Sabale, Moe Kayali, Dan Suciu
2024	J	`jnl`	Making LLMs Work for Enterprise Data Tasks. CoRR Çagatay Demiralp, Fabian Wenz, Peter Baile Chen, Moe Kayali, Nesime Tatbul, Michael Stonebraker
2024	J	`jnl`	Mind the Data Gap: Bridging LLMs to Enterprise Data Integration. CoRR Moe Kayali, Fabian Wenz, Nesime Tatbul, Çagatay Demiralp
2024	J	`jnl`	QirK: Question Answering via Intermediate Representation on Knowledge Graphs. CoRR Jan Luca Scheerer, Anton Lykov, Moe Kayali, Ilias Fountalis, Dan Olteanu, Nikolaos Vasiloglou, Dan Suciu
2023	J	`jnl`	CHORUS: Foundation Models for Unified Data Discovery and Exploration. CoRR Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, Dan Suciu
2022	J	`jnl`	Mining Robust Default Configurations for Resource-constrained AutoML. CoRR Moe Kayali, Chi Wang
2022	J	`jnl`	Quasi-stable Coloring for Graph Compression: Approximating Max-Flow, Linear Programs, and Centrality. CoRR Moe Kayali, Dan Suciu
2022	J	`jnl`	Quasi-stable Coloring for Graph Compression: Approximating Max-Flow, Linear Programs, and Centrality. Proc. VLDB Endow. Moe Kayali, Dan Suciu
2020	—	`conf`	Causal Relational Learning. SIGMOD Conference Babak Salimi, Harsh Parikh, Moe Kayali, Lise Getoor, Sudeepa Roy, Dan Suciu
2020	J	`jnl`	Causal Relational Learning. CoRR Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu
2020	J	`jnl`	Demonstration of Inferring Causality from Relational Databases with CaRL. Proc. VLDB Endow. Moe Kayali, Babak Salimi, Dan Suciu