Related papers: Poseidon: A OneGraph Engine
Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano.…
Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter…
We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations. A novel training strategy…
Amazon Neptune is a graph database service that supports two graph (meta)models: W3C's Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers opt in for one or the other model, and this choice determines which…
Real world graphs are often dynamic and evolve over time. It is crucial for storing and querying graph evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support,…
In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon…
Surrogate modeling of body-driven fluid flows where immersed moving boundaries couple structural dynamics to chaotic, unsteady fluid phenomena remains a fundamental challenge for both computational physics and machine learning. We present…
We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our…
The database community has long recognized the importance of graphical query interface to the usability of data management systems. Yet, relatively less has been done. We present Orion, a visual interface for querying ultra-heterogeneous…
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs,…
As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative…
Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the…
Over-smoothing remains a major challenge in Graph Neural Networks (GNNs), where repeated message passing causes node representations to converge and lose discriminative power. To address this, we propose a novel node selection strategy…
Graph databases have garnered extensive attention and research due to their ability to manage relationships between entities efficiently. Today, many graph search services have been outsourced to a third-party server to facilitate storage…
Advances in voice-controlled assistants paved the way into the consumer market. For professional or industrial use, the capabilities of such assistants are too limited or too time-consuming to implement due to the higher complexity of data,…
Partitioned global address space (PGAS) is a parallel programming model for the development of applications on clusters. It provides a global address space partitioned among the cluster nodes, and is supported in programming languages like…
Graphs are a ubiquitous data structure in diverse domains such as machine learning, social networks, and data mining. As real-world graphs continue to grow beyond the memory capacity of single machines, out-of-core graph processing systems…
Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the…
Datalog-based languages are regaining popularity as a powerful abstraction for expressing recursive computations in domains such as program analysis and graph processing. However, existing systems often face a trade-off between efficiency…
The Poisson-Fermi model is an extension of the classical Poisson-Boltzmann model to include the steric and correlation effects of ions and water treated as nonuniform spheres in aqueous solutions. Poisson-Boltzmann electrostatic…