An Open Semantic Framework for the Industrial Internet of Things

Mayer, Simon; Hodges, Jack; Yu, Dan; Kritzler, Mareike; Michahelles, Florian

doi:10.1109/mis.2017.9

Cited by 76 publications

(35 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the proposed framework is limited for validating and annotating IoT dynamic data efficiently. Mayer et al [25] developed an Open Semantic Framework (OSF) for industrial IoT applications to make the web of things into semantic web of things. This framework is widely designed to enable the industrial things with semantics to the IoT domains.…”

Section: Background and Related Workmentioning

confidence: 99%

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Núñez‐Valdéz¹,

Solanki²,

Balakrishna³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHC-AA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely-Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Núñez‐Valdéz¹,

Solanki²,

Balakrishna³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

show abstract

“…The Semantic Sensor Network (SSN) [18], an ontology specialized for describing sensors, is one example of a domain-specific ontology. The Open Semantic Framework (OSF) [19] combines many such specific ontologies into an extendable framework, fusing both general and domain specific knowledge. Ontologies form the core of semantic technologies, but not all ontologies can be combined and function together.…”

Section: Related Work On Interoperability Solutionsmentioning

confidence: 99%

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Nilsson

Sandin

Delsing

2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

View full text Add to dashboard Cite

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

show abstract

“…Many papers have proposed internal modeling of IoT services [8][9][10][11]. These studies proposed a semantic modeling approach to integrate the IoT framework into the IoT services by automatically obtaining the data from the mobile devices and the sensors [9,10].…”

Section: Iot Servicementioning

confidence: 99%

Future of IoT Networks: A Survey

2017

View full text Add to dashboard Cite

Abstract:The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT), our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

show abstract

An Open Semantic Framework for the Industrial Internet of Things

Cited by 76 publications

References 6 publications

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Future of IoT Networks: A Survey

Contact Info

Product

Resources

About