10 citations · 16 across the 7 of their papers we have counts for
9 papers
Chronos: Efficient Bolt-on Branching Across Data Stores for Stateful Agentic Applications
Xinjing Zhou, Jason Mohoney, Samuel Madden +2
Data-centric applications increasingly use speculative execution to explore multiple candidate paths where each path modifies state distributed across heterogeneous data stores. Th…
Guarded Commits: Transactional Human Approvals for LLM Workflows
Laurent Bindschaedler, Ferdi Kossmann, Chunwei Liu +1
LLM workflows often require human approval before an irreversible external action. Most systems keep that approval outside the workflow, as an interface click or an audit entry. Th…
Archi: Agentic Operations at the CMS Experiment
Pietro Lugato, Luca Lavezzo, Jason Mohoney +16
We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the…
Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang +5
Vector search, the task of finding the k-nearest neighbors of a query vector against a database of high-dimensional vectors, underpins many machine learning applications, including…
Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
Roger Waleffe, Devesh Sarda, Jason Mohoney +3
We study distributed training of Graph Neural Networks (GNNs) on billion-scale graphs that are partitioned across machines. Efficient training in this setting relies on min-edge-cu…
Incremental IVF Index Maintenance for Streaming Vector Search
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury +7
The prevalence of vector similarity search in modern machine learning applications and the continuously changing nature of data processed by these applications necessitate efficien…
High-Throughput Vector Similarity Search in Knowledge Graphs
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury +5
There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems…
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas +1
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of using distributed training for billion-scale graphs and show that for graphs tha…
Marius: Learning Massive Graph Embeddings on a Single Machine
Jason Mohoney, Roger Waleffe, Yiheng Xu +2
We propose a new framework for computing the embeddings of large-scale graphs on a single machine. A graph embedding is a fixed length vector representation for each node (and/or e…