CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion
This work addresses the challenge of integrating and processing streaming data with semantic technologies for applications in IoT and distributed systems, representing a significant incremental advancement over the previous version.
The paper tackles the problem of semantic stream fusion by introducing CQELS 2.0, a platform-agnostic federated execution framework that includes a novel neural-symbolic stream reasoning component and an adaptive federator, enabling DNN-based data fusion via logic rules with learnable weights and resource coordination across network nodes.
We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. As a platform-agnostic framework, CQELS 2.0 can be implemented for devices with different hardware architectures (from embedded devices to cloud infrastructures). Moreover, this version also includes an adaptive federator that allows CQELS instances on different nodes in a network to coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries