A scalable semantic data fusion framework for heterogeneous sensors data

Al-Baltah, Ibrahim Ahmed; Ghani, Abdul Azim Abd; Al-Gomaei, Ghilan Mohammed; Abdulrazzak, Fua’ad Hassan; Kharusi, Abdulmonem Ali Al

doi:10.1007/s12652-020-02527-5

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…In [28], the authors analyzed the detection process of massage chair intelligent detection robot and made theoretical research from decision-level fusion and data-level fusion. Data generated by sensors in the Internet of ings always is large-scale, multisource, and heterogeneous, so data fusion is necessary for providing intelligent services [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

AI-Based Heterogenous Large-Scale English Translation Strategy

Wang

2022

Mobile Information Systems

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English has become one of the most widely used languages in the world. If there is no good translation mechanism for such a widely used language, it will bring trouble to both study and life. At present, the world’s major platforms are committed to the study of English translation strategies. There are translation platforms from different regions and different translation mechanisms. These translation data from different translation platforms have the characteristics of large-scale, multisource, heterogeneity, high dimensions, and poor quality. However, such inconsistent translation data will increase the translation difficulty and translation time. Therefore, it is necessary to improve the quality of translation data to achieve a better translation effect. How to provide a large-scale and efficient translation strategy needs to integrate the translation strategies of various platforms to perform heterogeneous translation data cleaning and fusion based on machine learning. At first, this paper represents the multisource, heterogeneous translation data model as tree-augmented naive Bayes networks (TANs) and naturally captures the relationship between the datasets through the learning of TANs structure and the probability distribution of input attributes and tuples, using data probability value to complete the classification of translation data cleaning. Then, a multisource, heterogeneous translation data fusion model based on recurrent neural network (RNN) is constructed, and RNN is used to control the node data of hidden layer to enhance the fault-tolerant ability in the fusion process and complete the construction of fusion model. Finally, experimental results show that TANs-based translation data cleaning method can effectively improve the cleaning rate with an average improvement of approximately 10% and cleaning time with an average reduce about 5%. In addition, RNN-based multisource translation data fusion method improves the shortcomings of the traditional fusion model and improves the practicability of the fusion model in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), fusion time, and integrity.

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Section: Related Workmentioning

confidence: 99%

AI-Based Heterogenous Large-Scale English Translation Strategy

Wang

2022

Mobile Information Systems

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“…SW is an upgraded version of the current web, which publishes the data in a machine-readable, understandable, and processable format through defining ontologies [2]. Researchers propose a Semantic Web of Things (SWoT) framework that supports the collaborative discovery of sensors and actuators for semantic matchmaking [3] and a data fusion framework for improving scalability. Semantic annotation of sensor data consists of metadata information in triples, making the data bulkier.…”

Section: Introductionmentioning

confidence: 99%

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

Velu¹,

Thangavelu²

2022

Computers, Materials &Amp; Continua

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The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology's quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D's ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

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