A live context model for semantic reasoning in IoT applications

Gaudio, Daniel Del; Ariguib, Boshra; Bartenbach, Arne; Solakis, Georgios

doi:10.1109/percomworkshops53856.2022.9767267

Cited by 2 publications

(6 citation statements)

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“…1) Context modeling: The ontology-based context model must be manually/automatically generated in an offline phase (It has been generated automatically for our Smart Home use case). The concept to monitor an environment, detect changes and update the context model constantly, using different adapters, relies on our previous work [7] and is therefore beyond the scope of this concept. However, it is important to notice, that in our concept, context changes can originate from multiple different sources, e.g., by the interaction of human users via a user interface.…”

Section: System Model and Architecturementioning

confidence: 99%

“…In this paper, we assume that RTClean receives a data set, that contains errors, and are able to create an ontologybased context model that describes the structure of the data or of the environment which the data is extracted from. For a better understanding of the remainder of this paper, Section III gives an overview of the context model described in [7] together with the extensions we made to use the context model for data cleaning.…”

Section: Downstream Applicationsmentioning

confidence: 99%

“…OFDs can then be used to generate additional features from the model. For the context model, we use the live context model approach that we proposed in our previous work [7], which builds on the ontologies IoT-Lite [9] and SSN [10]. The use of ontologies enables the extensibility of the context model to different data and use cases.…”

Section: Ontology-based Context Modelmentioning

confidence: 99%

“…Such fresh context information enables us to adapt the data cleaning automatically to changes of the environment in real-time. We extended the ontology proposed in [7] for the purpose of data cleaning. is originated.…”

Section: Ontology-based Context Modelmentioning

confidence: 99%

“…dBoost: https://github.com/cpitclaudel/dBoost6 Apache Jena: https://jena.apache.org/index.html7 Apache Fuseki: https://jena.apache.org/documentation/fuseki2/index.html…”

mentioning

confidence: 99%

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RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs

Gaudio¹,

Schubert²,

Abdelaal³

2023

Preprint

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Nowadays, machine learning plays a key role in developing plenty of applications, e.g., smart homes, smart medical assistance, and autonomous driving. A major challenge of these applications is preserving high quality of the training and the serving data. Nevertheless, existing data cleaning methods cannot exploit context information. Thus, they usually fail to track shifts in the data distributions or the associated error profiles. To overcome these limitations, we introduce, in this paper, a novel method for automated tabular data cleaning powered by dynamic functional dependency rules extracted from a live context model. As a proof of concept, we create a smart home use case to collect data while preserving the context information. Using two different data sets, our evaluations show that the proposed cleaning method outperforms a set of baseline methods in terms of the detection and repair accuracy.

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Section: System Model and Architecturementioning

confidence: 99%

Section: Downstream Applicationsmentioning

confidence: 99%

Section: Ontology-based Context Modelmentioning

confidence: 99%

Section: Ontology-based Context Modelmentioning

confidence: 99%

“…dBoost: https://github.com/cpitclaudel/dBoost6 Apache Jena: https://jena.apache.org/index.html7 Apache Fuseki: https://jena.apache.org/documentation/fuseki2/index.html…”

mentioning

confidence: 99%

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RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs

Gaudio¹,

Schubert²,

Abdelaal³

2023

Preprint

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Improved semantic application to perception and execution for Internet of Things

Zhang,

Shao,

Han

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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The Internet of Things is a rapidly growing field that promises to revolutionize how we interact with and control devices in our environment. As the number of Internet of Things devices increases, the need for efficient and intelligent ways to manage devices becomes increasingly important. However, the heterogeneity and scale of Internet of Things devices pose significant challenges to the effective integration and management. Semantic technology provide a solution by enabling devices to understand and interpret user intentions more accurately. In this study, an improved semantic model was designed based on related concepts and application of semantics in perception and execution for the Internet of Things. The proposed semantic frameworks was highlighted in enabling device-to-device communication and data aggregation, facilitating a holistic understanding of user needs. The importance of open standards and interoperability was also emphasized in realizing the full potential of semantic technology in the Internet of Things. The understanding and control of Internet of Things devices can be improved based on semantic technology, leading to more efficient and user-friendly systems.

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A live context model for semantic reasoning in IoT applications

Cited by 2 publications

References 10 publications

RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs

RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs

Improved semantic application to perception and execution for Internet of Things

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