Semantic-aware data quality assessment for image big data

Yu, Li; Wang, Yangtao; Zhou, Ke; Yu-juan, Yang; Liu, Yifei

doi:10.1016/j.future.2019.07.063

Cited by 19 publications

(5 citation statements)

References 8 publications

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“…11 For example, huge data information needs more human resources to process, and the processed data needs to be further labelled by experienced experts, which undoubtedly adds a lot of burden to researchers. Moreover, the emergence of big data 12 and cloud computing 13,14 provides a guarantee for the storage and processing of visual data, especially provides plenty of fuels for data-driven analysis. As one of the most important methods, AI 15 receives more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration.

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Section: Introductionmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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“…The semantic knowledge data quality architecture assessment that is supplied by Liu et al, 2020 [34] enables users to grasp the semantic value of image big data in connection to requests and the origin of ideas. This is made possible by the fact that users are able to understand the information.…”

Section: Related Workmentioning

confidence: 99%

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Amanoul,

Abdulrahman,

Abdullah

et al. 2023

Journal of Smart Internet of Things

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Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.

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“…Step 4: Calculate the maximum eigenvalue max according to the following formula. The formula is given by Equation (10).…”

Section: Determination Of the Subjective Weight Of Indicators Based On Ahpmentioning

confidence: 99%

“…Many developed countries have successively issued relevant policies to promote the development of big data, and have promoted big data as a national strategy [4][5][6]. Through big data analysis, it is possible to summarize experience, discover laws, predict trends, assist decision-making, release and utilize the huge value contained in data resources, provide convenience for people's lives, provide value for business activities, and provide strategic opportunities for national development [7][8][9][10]. However, the value of big data is based on accurate, comprehensive, and high-quality data.…”

Section: Introductionmentioning

confidence: 99%

Research on Comprehensive Evaluation of Data Source Quality in Big Data Environment

Li¹,

Xu²,

Peng³

2021

IJCIS

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Data quality is the prerequisite of big data research and the basis of all data analysis, mining, and decision support. Therefore, a comprehensive fuzzy evaluation method for big data quality evaluation is proposed. Through the analysis of big data quality characteristics, a big data quality evaluation system for the whole process of data processing is constructed. The subjective weight and objective weight of each indicator are calculated through the analytic hierarchy process and entropy method. In order to overcome the subjective and one-sided shortcomings of the single weight determination method, the subjective weight and the objective weight are organically integrated through the distance function method to determine the combined weight of each indicator. The quantified result of big data quality is obtained through fuzzy calculation of membership degree. Finally the ranking results of the proposed method are compared with those of some existing multi-attribute decision-making (MADM) methods. The obtained results indicate that the proposed method is reasonable and efficient to deal with MADM problems. It can comprehensively measure the level of big data quality, and provide users with accurate and efficient quality evaluation results.

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Semantic-aware data quality assessment for image big data

Cited by 19 publications

References 8 publications

Deep learning for visual recognition and detection of aquatic animals: A review

Deep learning for visual recognition and detection of aquatic animals: A review

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Research on Comprehensive Evaluation of Data Source Quality in Big Data Environment

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