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Big Data Analytics and Edge Computing: Recent Trends and Future
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Big Data Analytics and Edge Computing: Recent Trends and Future

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 6151

Special Issue Editors

Dr. Li Xiao

E-Mail Website
Guest Editor
Lead Editor, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Interests: multimodal computing; medical images; smart healthcare; edge computing and data analytics; scientific intelligence
Dr. Zhu Meng

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Interests: computer vision; medical image processing; smart healthcare; edge computing and data analytics
Dr. Zichun Yan

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Interests: digital economy; digital technology innovation
Dr. Minling Zhu

E-Mail Website
Guest Editor
Computer School, Beijing Information Science and Technology University, Beijing 100101, China
Interests: AI; computer vision
Dr. Jose Manuel Oliveira

E-Mail Website
Guest Editor
1. Faculty of Economics, University of Porto, 4200-464 Porto, Portugal
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: time series forecasting; machine learning; deep learning; data science; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the special issues of Big Data and Cognitive Computing: entitled “Big Data Analytics and Edge Computing: Recent Trends and Future”. The rapid development of large models brings significant technical improvement to big data analytics. Meanwhile, the development of edge computing also makes the application of big data analytical models more convenient and rapid.

The main focus of this Special Issue lies in the exploration and presentation of cutting-edge technologies at the intersection of big data analytics and edge computing. Delving into the intricacies of this dynamic field, we aim to showcase the latest advancements encompassing large models, multi-modal fusion, and an array of innovative edge computing technologies, including but not limited to image processing, video understanding, and sound recognition. Our overarching objective is to unveil pioneering technological solutions that not only drive the evolution of big data tools but also significantly impact the outcomes of big data platforms integrated with edge computing infrastructure. Beyond merely showcasing technological advancements, this Special Issue serves as a platform for researchers to delve into specific areas of big data application. We actively encourage contributions that explore the transformative potential of big data analytics in domains as diverse as healthcare, digital economics, environmental protection, etc. Through comprehensive research inquiries and insightful analyses, we aim to foster a rich dialogue that transcends disciplinary boundaries, paving the way for innovative solutions and impactful outcomes in the realm of big data and edge computing.

In this Special Issue, both original research articles and reviews are welcome. Research topics include, but are not limited to, the following topics:

  • Computer vision;
  • Natural language processing;
  • Large-scale foundation model;
  • Big data analytics in healthcare;
  • Privacy-preserving techniques in big data analytics and deep learning;
  • Blockchain and AI integration;
  • AI-powered vital sign analytics;
  • Big data and deep learning in low-carbon traffic and smart traffic;
  • Big data analysis in economics and finance;
  • Analysis of artificial intelligence model for edge computing;
  • Forecasting of large model and deep learning;
  • Digital twins and 3D reconstruction;
  • Reinforcement learning for big data analysis. 

Dr. Li Xiao
Dr. Zhu Meng
Dr. Zichun Yan
Dr. Minling Zhu
Dr. Jose Manuel Oliveira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analytics
  • edge computing
  • large models
  • multi-modal fusion
  • image processing
  • video understanding
  • sound recognition

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Published Papers (5 papers)

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Research

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12 pages, 1482 KiB  
Article
Semi-Open Set Object Detection Algorithm Leveraged by Multi-Modal Large Language Models
by Kewei Wu, Yiran Wang, Xiaogang He, Jinyu Yan, Yang Guo, Zhuqing Jiang, Xing Zhang, Wei Wang, Yongping Xiong, Aidong Men and Li Xiao
Big Data Cogn. Comput. 2024, 8(12), 175; https://doi.org/10.3390/bdcc8120175 - 29 Nov 2024
Viewed by 527
Abstract
Currently, closed-set object detection models represented by YOLO are widely deployed in the industrial field. However, such closed-set models lack sufficient tuning ability for easily confused objects in complex detection scenarios. Open-set object detection models such as GroundingDINO expand the detection range to [...] Read more.
Currently, closed-set object detection models represented by YOLO are widely deployed in the industrial field. However, such closed-set models lack sufficient tuning ability for easily confused objects in complex detection scenarios. Open-set object detection models such as GroundingDINO expand the detection range to a certain extent, but they still have a gap in detection accuracy compared with closed-set detection models and cannot meet the requirements for high-precision detection in practical applications. In addition, existing detection technologies are also insufficient in interpretability, making it difficult to clearly show users the basis and process of judgment of detection results, causing users to have doubts about the trust and application of detection results. Based on the above deficiencies, we propose a new object detection algorithm based on multi-modal large language models that significantly improves the detection effect of closed-set object detection models for more difficult boundary tasks while ensuring detection accuracy, thereby achieving a semi-open set object detection algorithm. It has significant improvements in accuracy and interpretability under the verification of seven common traffic and safety production scenarios. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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15 pages, 4071 KiB  
Article
Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots
by Li Wan, Zhenjiang Li, Changan Zhang, Guangyong Chen, Panming Zhao and Kewei Wu
Big Data Cogn. Comput. 2024, 8(11), 147; https://doi.org/10.3390/bdcc8110147 - 28 Oct 2024
Viewed by 596
Abstract
Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate [...] Read more.
Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in an edge computing mobile device using an intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an improvement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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22 pages, 9693 KiB  
Article
A Trusted Supervision Paradigm for Autonomous Driving Based on Multimodal Data Authentication
by Tianyi Shi, Ruixiao Wu, Chuantian Zhou, Siyang Zheng, Zhu Meng, Zhe Cui, Jin Huang, Changrui Ren and Zhicheng Zhao
Big Data Cogn. Comput. 2024, 8(9), 100; https://doi.org/10.3390/bdcc8090100 - 2 Sep 2024
Viewed by 1016
Abstract
At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by [...] Read more.
At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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18 pages, 4725 KiB  
Article
Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix
by Wenxuan Zhang and Benzhuo Lu
Big Data Cogn. Comput. 2024, 8(6), 56; https://doi.org/10.3390/bdcc8060056 - 30 May 2024
Cited by 1 | Viewed by 2026
Abstract
Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall [...] Read more.
Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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Review

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14 pages, 253 KiB  
Review
Feasibility Study of Edge Computing Empowered by Artificial Intelligence—A Quantitative Analysis Based on Large Models
by Yan Chen, Chaonan Wu, Runqi Sui and Jingjia Zhang
Big Data Cogn. Comput. 2024, 8(8), 94; https://doi.org/10.3390/bdcc8080094 - 19 Aug 2024
Viewed by 1053
Abstract
The advancement of artificial intelligence (AI) demands significant data and computational resources that have an adverse impact on the environment. To address this issue, a novel computing architecture that is both energy efficient and eco-friendly is urgently required. Edge computing has emerged as [...] Read more.
The advancement of artificial intelligence (AI) demands significant data and computational resources that have an adverse impact on the environment. To address this issue, a novel computing architecture that is both energy efficient and eco-friendly is urgently required. Edge computing has emerged as an increasingly popular solution to this problem. In this study, we explore the development history of edge computing and AI and analyze the potential of model quantization to link AI and edge computing. Our comparative analysis demonstrates that the quantization approach can effectively reduce the model’s size and accelerate model inference while maintaining its functionality, thereby enabling its deployment on edge devices. This research serves as a valuable guide and reference for future advancements in edge AI. Full article
(This article belongs to the Special Issue Big Data Analytics and Edge Computing: Recent Trends and Future)
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