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CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

arXiv 2202.13958 ↗First posted 15 Feb 2022cs.DB cs.AIcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 15 Feb 2022.

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9 of 27 references.

References · 9

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  5. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 910
  6. 2016Simple Online and Realtime Trackingcited by 868
  7. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  8. 2019Deep Learning in Video Multi-Object Tracking: A Surveycited by 107
  9. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 76

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