Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach

Ryabinin, Konstantin; Чуприна, Светлана; Labutin, Ivan

doi:10.1007/978-3-030-89477-1_9

Cited by 7 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Main focus points of this paper are devoted to tackling the following problems in order to improve the applicability of scientific visualization tools of SciVi developed by our team previously [1,2,3,4] in BCI studies to adapt them to the specific of the different IoT infrastructure, models and datasets:…”

Section: Key Contributionsmentioning

confidence: 99%

“…As a part of our previous work, we built and successfully tested a pipeline that utilizes a unified high-level mechanism to allow brain-computer interfaces to be integrated into diverse IoT ecosystems in a way that doesn't depend on the characteristics of a particular ecosystem [4]. That pipeline was developed using the SciVi platform, an ontology-driven scientific visualization and visual analytics toolset [1].…”

Section: Fig 1 a Common Scheme Of Neuroscience Experiments Involving ...mentioning

confidence: 99%

“…2 shows an example of such ontological description used in our experiments (21-channel EB-Neuro headset montage). As usual, we restrict the set of relation types used in the ontology only by paradigmatic types ("is a", "has", "is used for") in order to reduce the complexity of the reasoner allowing to embed it to Edge devices as firmware [4], which ensures semantic interoperability within IoT networks. And we used visual editor ONTOLIS to create ontologies.…”

Section: Decoupling Headset Information From Signalmentioning

confidence: 99%

See 2 more Smart Citations

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

Чуприна¹,

Labutin²

2022

View full text Add to dashboard Cite

The technological progress in the field of Brain-Computer Interface and its integration with IoT have now put on the agenda the question of the fast transition of the technology from laboratory experiments into everyday life. The paper presents an approach to improve utilizing neural interface with the help of ontology-driven scientific visualization tools taking into account the urgent problems of automatic adaptation to the specifics of different IoT infrastructure, models and datasets. Some issues of replicability and reproducibility of experiments are also under discussion in this paper. Using the principles of "clean-room reverse engineering" methodology to rewrite existing EEG device drivers we make it possible to embed visualization tools which dynamically render the streaming data coming from different EEG devices within a diverse IoT infrastructure without any legal complications.

show abstract

Section: Key Contributionsmentioning

confidence: 99%

Section: Fig 1 a Common Scheme Of Neuroscience Experiments Involving ...mentioning

confidence: 99%

Section: Decoupling Headset Information From Signalmentioning

confidence: 99%

See 1 more Smart Citation

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

Чуприна¹,

Labutin²

2022

View full text Add to dashboard Cite

show abstract

“…Once the DFD is done, the DFD to Ontology Converter transforms it into the set of Task Ontologies, which specify the particular operations to be performed by particular computing resources SciVi can reach in the active network [16]. The specified operations are derived from the DFD and, if needed, automatically supplemented by communication actions to transmit data between the involved computing nodes.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…SciVi is a Web application; its server is written in Python using Flask and its client is based on JavaScript, HTML5, and CSS3. SciVi supports microservices, which run on the client (written in any browser-compatible programming language, including JavaScript, TypeScript, GLSL, and WebAssembly), on the server (usually written in Python or organized as pre-compiled binary libraries with Python-compatible interface), or on the special external hardware (for that case, appropriate firmware and middleware are generated automatically [22,23]).…”

Section: Introductionmentioning

confidence: 99%

Automatic Areas of Interest Detector for Mobile Eye Trackers

Ryabinin

Alexeeva

Petrova

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

Self Cite

View full text Add to dashboard Cite

Thе paper deals with automatic areas of interest detection in video streams derived from mobile eye trackers. Defining such areas on a visual stimulus viewed by an informant is an important step in setting up any eye-tracking-based experiment. If the informant’s field of view is stationary, areas of interest can be selected manually, but when we use mobile eye trackers, the field of view is usually constantly changing, so automation is badly needed. We propose using computer vision algorithms to automatically locate the given 2D stimulus template in a video stream and construct the homography transform that can map the undistorted stimulus template to the video frame coordinate system. In parallel to this, the segmentation of a stimulus template into the areas of interest is performed, and the areas of interest are mapped to the video frame. The considered stimuli are texts typed in specific fonts and the interest areas are individual words in these texts. Optical character recognition leveraged by the Tesseract engine is used for segmentation. The text location relies on a combination of Scale-Invariant Feature Transform and Fast Library for Approximate Nearest Neighbors. The homography is constructed using Random Sample Consensus. All the algorithms are implemented based on the OpenCV library as microservices within the SciVi ontology-driven platform that provides high-level tools to compose pipelines using a data-flow-based visual programming paradigm. The proposed pipeline was tested on real eye tracking data and proved to be efficient and robust.

show abstract

Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach

Cited by 7 publications

References 33 publications

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

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

Automatic Areas of Interest Detector for Mobile Eye Trackers

Contact Info

Product

Resources

About