A unified ontology-based data integration approach for the internet of things

Swar, Ahmed Mohamed; Khoriba, Ghada; Belal, Mohamed

doi:10.11591/ijece.v12i2.pp2097-2107

Cited by 3 publications

(1 citation statement)

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“…Research on IoT data processing is increasingly gaining traction as the heterogeneity and interoperability challenges of IoT data complicate application development and data management. The increasing research on IoT data processing, due to its heterogeneity and interoperation difficulties, utilizes technologies such as linked data 27 and ontologies. 28 Ganzha and others achieved interoperability across multidomain IoT platforms, identifying ontologies useful for cross-domain development.…”

Section: Introductionmentioning

confidence: 99%

Clustering on heterogeneous IoT information network based on meta path

Zhao,

Zhang,

et al. 2024

Science Progress

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As the Internet and Internet of Things (IoT) continue to develop, Heterogeneous Information Networks (HIN) have formed complex interaction relationships among data objects. These relationships are represented by various types of edges (meta-paths) that contain rich semantic information. In the context of IoT data applications, the widespread adoption of Trigger-Action Patterns makes the management and analysis of heterogeneous data particularly important. This study proposes a meta-path-based clustering method for heterogeneous IoT data called I-RankClus, which aims to improve the modeling and analysis efficiency of IoT data. By combining ranking with clustering algorithms, the PageRank algorithm was used to calculate the intraclass influence of objects in the network. The HITS algorithm then transfers the influence to the core objects, thereby optimizing the classification of objects during the clustering process. The I-RankClus algorithm does not process each meta-path individually, but instead integrates multiple meta-paths to enhance the interpretability and clustering performance of the model. The experimental results show that the I-RankClus algorithm can process complex IoT datasets more effectively than traditional clustering methods and provide more accurate clustering outcomes. Furthermore, through a detailed analysis of meta-paths, this study explored the influence and importance of different meta-paths, thereby validating the effectiveness of the algorithm. Overall, the research presented in this paper not only improves the application effects of HINs in IoT data analysis but also provides valuable methods and insights for future network data processing.

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Section: Introductionmentioning

confidence: 99%