Ontology-Driven Edge Computing

Ryabinin, Konstantin; Чуприна, Светлана

doi:10.1007/978-3-030-50436-6_23

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…In the domain of device monitoring, Funika et al present an ontology-based approach to perform the monitoring of resource usage in multi-scale platforms [53]. Connecting the domains of device monitoring and IoT, Ryabinin et al demonstrate an ontology-based approach to manage and monitor resource-constrained Edge Computing devices [54]. The Comprehensive Ontology for IoT (COIoT) tries to build an interoperable knowledge base for IoT environments by reusing core concepts from existing ontologies and adds additional concepts to support the monitoring of context and services [55].…”

Section: Monitoring Ontologiesmentioning

confidence: 99%

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

De Brouwer,

De Turck,

Ongenae

2024

J Netw Syst Manage

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In the Internet of Things (IoT), semantic IoT platforms are often used to solve the challenges associated with the real-time integration of heterogeneous IoT sensor data, domain knowledge and context information. Existing platforms mostly have a static distribution and configuration of queries deployed on the platform's stream processing components. In contrast, the environmental context in which queries are deployed has a very dynamic nature: real-world set-ups involve varying tasks, device resource usage, networking conditions, etc. To solve this mismatch, this paper presents DIVIDE, an IoT platform component built on Semantic Web technologies. DIVIDE has a generic design containing multiple subcomponents that monitor the environment across a cascading architecture. By monitoring the use case context, DIVIDE adaptively derives the appropriate stream processing queries in a context-aware way. Using a Local Monitor deployed on edge devices, situational context parameters are measured and aggregated. The Meta Model allows modeling these measurements, and meta-information about devices and deployed stream processing queries. Through the definition of application-specific Global Monitor queries that are continuously evaluated centrally on the Meta Model, end users can dynamically configure how the situational context should influence the window parameter configuration and distribution of queries in the network. The paper evaluates a first implementation of DIVIDE on a homecare monitoring use case. The results show how DIVIDE can successfully adapt to varying device and networking conditions, taking into account the use case requirements. This way, DIVIDE allows better balancing use case specific trade-offs and achieves more efficient stream processing.

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Section: Monitoring Ontologiesmentioning

confidence: 99%

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

De Brouwer,

De Turck,

Ongenae

2024

J Netw Syst Manage

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“…They are software containers for each involved computing node to run the chain of appropriate operators within. Each Executor generates a specific result, for example, Interactive Visualization to graphically depict the processed data, Middleware to communicate with third-party software or hardware systems, or Firmware to configure devices within the ecosystem of the Internet of Things [17].…”

Section: Multi-purpose Ontology-driven Data Flow Programming Platformmentioning

confidence: 99%

Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Ryabinin

Chumakov

Белоусов

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Visualization is claimed as one of the essential “V’s” of Big Data since it allows presenting the data in a human-friendly way and is, therefore, a stepping-stone for the Big Data mining process. Visual analytics, in turn, ensures knowledge discovery out of the data through cognitive graphics and filtering capabilities. But to be efficient, visualization and analytics tools have to consider other Big Data “V’s” by handling the large data volumes, keeping up with the data growth and changing velocity, and adapting to the variety of the data representation formats. We propose using ontology engineering methods to create a visual analytics platform controlled by an ontological knowledge base that describes supported data types, input formats, data filters, visual objects, and visualization algorithms, as well as available communication protocols and computing nodes, the platform modules can run on. This allows introducing new functions and distributed computation scenarios to the platform on the fly just by extending the underlying domain ontologies without changing the source code of the platform’s core. The analytics flow inside this platform is described by task ontologies enabling semantic data mining process. As a result, seamless integration with different data sources is achieved, including plain files, databases, and even third-party soft- and hardware solvers. We demonstrate the viability of the approach proposed by solving several data mining and fuzzy classification problems, including the assessment of the citizens’ regional identity according to the mental maps they draw and the reconstruction of ontogenesis of extinct synapsid ‘Titanophoneus potens’ Efremov, 1938.

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“…The thesaurus specifies BCI-related concepts, such as "EEG device", "EEG channel", "EEG electrode", etc. In order to reduce the complexity of the ontology reasoner allowing to embed it to Edge devices as firmware [17], we restricted the set of relation types of BCI-O by the paradigmatic types only, such as "has", "a_part_of", "use", "use_for", "is_instance", and "is_a". The fragment of proposed ontology is shown in Fig.…”

Section: Ontology-driven Pipeline Processingmentioning

confidence: 99%

Ontology-Driven Toolset for Audio-Visual Stimuli Representation in EEG-Based BCI Research

Ryabinin

Чуприна

Labutin

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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In the last decade, the recent advances in software and hardware facilitate the increase of interest in conducting experiments in the field of neurosciences, especially related to human-machine interaction. There are many mature and popular platforms leveraging experiments in this area including systems for representing the stimuli. However, these solutions often lack high-level adaptability to specific conditions, specific experiment setups, and third-party software and hardware, which may be involved in the experimental pipelines. This paper presents an adaptable solution based on ontology engineering that allows creating and tuning the EEG-based brain-computer interfaces. This solution relies on the ontology-driven SciVi visual analytics platform developed earlier. In the present work, we introduce new capabilities of SciVi, which enable organizing the pipeline for neuroscience-related experiments, including the representation of audio-visual stimuli, as well as retrieving, processing, and analyzing the EEG data. The distinctive feature of our approach is utilizing the ontological description of both the neural interface and processing tools used. This increases the semantic power of experiments, simplifies the reuse of pipeline parts between different experiments, and allows automatic distribution of data acquisition, storage, processing, and visualization on different computing nodes in the network to balance the computation load and to allow utilizing various hardware platforms, EEG devices, and stimuli controllers.

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Ontology-Driven Edge Computing

Cited by 10 publications

References 18 publications

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDE

Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Ontology-Driven Toolset for Audio-Visual Stimuli Representation in EEG-Based BCI Research

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