Autonomous RDF Stream Processing for IoT Edge Devices

Nguyen-Duc, Manh; Le-Tuan, Anh; Calbimonte, Jean-Paul; Hauswirth, Manfred; Phuoc, Danh Le

doi:10.1007/978-3-030-41407-8_20

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…For scalability, CQELS employs Storm and HBase as underlying software stacks for coordinating parallel execution processes to build an RSP engine on the cloud computing infrastructure, called CQELS Cloud [16]. To tailor the RDF-based data processing operations on edge devices (e.g, ARM CPU, Flash-storage), CQLES can be integrated in RDF4Led [17], a RISC style RDF engine for lightweight edge devices, to build Fed4Edge [20]. The whole Fed4Edge is smaller than 10MB and needs only 4-6 MB of RAM to process millions of triples on various small devices such as BeagleBone, Raspberry PI.…”

Section: System Architecture Overviewmentioning

confidence: 99%

“…Therefore, the existing framework is no longer suitable for the new application scenarios and requires significant extensions. Towards this goal, in CQELS 2.0, we integrate a number of new stream data types such as video streams, LiDARs, and support more hardwares such as ARM and mobiles [17,20]. Moreover, we provide data fusion operations in our engines [15,21].…”

Section: Introductionmentioning

confidence: 99%

“…DFG COSMO and BMBF BIFOLD ) and industry partners. For example, application scenarios in edge intelligence [26,9] and industry 4.0 from DellEMC, Siemens and Bossh motivate us to build autonomous processing kernels powered by CQELS [20,21]. Such systems deal with city-scale camera deployments which have become ubiquitous in smart cities and traffic monitoring with a continuously increasing in size and utilization of their deployments.…”

Section: Introductionmentioning

confidence: 99%

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CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

Le-Tuan¹,

Nguyen-Duc²,

Le³

et al. 2022

Preprint

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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 neuralsymbolic 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.

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Section: System Architecture Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

Le-Tuan¹,

Nguyen-Duc²,

Le³

et al. 2022

Preprint

Self Cite

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“…Semantic stream processing and reasoning are getting more and more attention in various application domains such as IoT, Industry IoT, and Smart Cities [18,20,21,4,26]. Among them, many recent works, e.g.…”

Section: Motivationmentioning

confidence: 99%

“…Note that using RDF-based symbols from such vocabularies can facilitate semantic interoperability across distributed ROS nodes, called Semantic Nodes. With the declarative continuous query language [16,9] , the query federation feature in [26] and [27] is aligned with the publish/subscribe mechanism along with distributed data distribution abstraction, such as Data Distribution Service (DDS) of ROS. For example, the following CQELS-QL query in Listing 1 will subscribe to a continuous query to fuse two videos, and publish a new stream as a ROS node, generating bounding boxes of "traffic obstacles" utilizing object detection models.…”

Section: Semantic Stream Reasoning Towards Ros-based Abstractionsmentioning

confidence: 99%

SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems

Nguyen-Duc¹,

Le-Tuan²,

Hauswirth³

et al. 2022

Preprint

Self Cite

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Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/semantic streams with deep neural networks, to integrate high-dimensional data streams, such as video streams and LiDAR point clouds, with traditional graph or relational stream data. As such, this positioning and system paper will outline our approach to build a platform to facilitate semantic stream reasoning capabilities on a robotic operating system called SemRob. MotivationSemantic stream processing and reasoning are getting more and more attention in various application domains such as IoT, Industry IoT, and Smart Cities [18,20,21,4,26]. Among them, many recent works, e.g. [35,17], were motivated by several use cases in autonomous driving and robotics. In parallel, reasoning and knowledge-based features attracted many research in robotics such as KnowRob [2] and semantics for robotic mapping, perception, and interaction [11]. Moreover, various works, e.g [25,14], on symbolic planning for robotics have a lot of connections to semantic streams and reasoning.

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RoXi: A Framework for Reactive Reasoning

Bonte,

Ongenae

2023

Lecture Notes in Computer Science

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The Stream Reasoning research paradigm aims to target application domains that need to solve both data variety and velocity at the same time. Many of these domains benefit from processing the data as close to the source as possible, e.g. in Internet of Things applications we see a paradigm shift towards Edge processing, and in the Web, we see more and more effort towards decentralization of the Web where applications run directly inside the browser. However, current Stream Reasoning engines are not able to reuse the same code-base to run applications in the cloud, edge, or browser. In this paper, we present RoXi, a Reactive Reasoning framework that provides the needed building blocks to realize Stream Reasoning applications that can target cloud, edge, and browser environments.

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Autonomous RDF Stream Processing for IoT Edge Devices

Cited by 7 publications

References 20 publications

CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

CQELS 2.0: Towards A Unified Framework for Semantic Stream Fusion

SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems

RoXi: A Framework for Reactive Reasoning

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