157 citations · 236 across the 10 of their papers we have counts for
12 papers
Koala: Non-disruptive Reconfiguration for Long-running Stateful Dataflows
Lei Huang, Sakshi Sharma, Chenxu Yu +5
Stateful dataflow systems must decide where state lives relative to the tasks that process it. For long-running applications that require frequent reconfiguration due to changes in…
CryptDough: A Unified Analytics Engine for Secure Multiparty Computation
Muhammad Faisal, Alessandra Lanz, Sam Buxbaum +4
We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeli…
ConRAD: Conformal Risk-Aware Neural Databases
Sonia Horchidan, Fabian Zeiher, Xiangyu Shi +4
Querying incomplete knowledge graphs with neural predictors is powerful but dangerous. Errors compound across multi-hop pipelines with no formal bound on the completeness of result…
Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
Yuhang Song, Naima Abrar Shami, Romaric Duvignau +1
As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods. While…
ORQ: Complex Analytics on Private Data with Strong Security Guarantees
Eli Baum, Sam Buxbaum, Nitin Mathai +4
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-…
Secrecy: Secure collaborative analytics on secret-shared data
John Liagouris, Vasiliki Kalavri, Muhammad Faisal +1
We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may n…
The Future is Big Graphs! A Community View on Graph Processing Systems
Sherif Sakr, Angela Bonifati, Hannes Voigt +38
Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users a…
A Survey on the Evolution of Stream Processing Systems
Marios Fragkoulis, Paris Carbone, Vasiliki Kalavri +1
Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and n…
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems
Maciej Besta, Marc Fischer, Vasiliki Kalavri +2
Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and…
Megaphone: Latency-conscious state migration for distributed streaming dataflows
Moritz Hoffmann, Andrea Lattuada, Frank McSherry +3
We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Meg…
DeltaPath: dataflow-based high-performance incremental routing
Desislava Dimitrova, John Liagouris, Sebastian Wicki +3
Routing controllers must react quickly to failures, reconfigurations and workload or policy changes, to ensure service performance and cost-efficient network operation. We propose…
High-Level Programming Abstractions for Distributed Graph Processing
Vasiliki Kalavri, Vladimir Vlassov, Seif Haridi
Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled…