S-ToPSS: Semantic Toronto Publish/Subscribe System
Milenko Petrovic, Ioana Burcea, Hans-Arno Jacobsen
Abstract
The increase in the amount of data on the Internet has led to the development of a new generation of applications based on selective information dissemination where, data is distributed only to interested clients. Such applications require a new middleware architecture that can efficiently match user interests with available information. Middleware that can satisfy this requirement include event-based architectures such as publish-subscribe systems. In this demonstration paper we address the problem of semantic matching. We investigate how current publish/subscribe systems can be extended with semantic capabilities. Our main contribution is the development and validation (through demonstration) of a semantic pub/sub system prototype S-ToPSS (Semantic Toronto Publish/Subscribe System).
Create a lesson
Related papers
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun et al.
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić et al.
Accelerating Sharded Data Parallelism at Scale with Federated Learning
Gianluca Mittone, Marco Aldinucci
Distributed Edge Inference: an Experimental Study on Multiview Detection
Gianluca Mittone, Giulio Malenza, Marco Aldinucci et al.
P-GADMM: Parallel Group-Based ADMM for Asynchronous Optimization in Heterogeneous Edge Networks
Gaiguo Wei, Qingying Zhang, Heqiang Wang et al.
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng