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Applied Sciences
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Advances in Big Data Analysis and Visualization
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Advances in Big Data Analysis and Visualization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 5445

Special Issue Editors

Dr. Yong Zhang

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: big data analysis; artificial intelligence; intelligent transportation; graphics and images
Prof. Dr. Yanming Shen

E-Mail Website
Guest Editor
College of Computer Science and Technology, Dalian University of Technology, Dalian 116000, China
Interests: machine learning; knowledge graph; big data

Special Issue Information

Dear Colleagues,

This is a call for papers on the topic of “Advances in Big Data Analysis and Visualization”, which has been designed in order to provide counterparts and decision-makers with guidelines to solve the bottlenecks and technical challenges faced by the field of big data and visualization.

In the contemporary world, the rapid development of information technology has given rise to huge amounts of data beyond any era. Proper visualization methods can help human beings uncover the hidden information behind big data. Big data visualization refers to the automatic analysis and mining method of big data, while effectively integrating the computing power of computers to obtain insights into large-scale complex data sets. In recent years, visualization research has largely focused on hot areas of big data, such as the internet, urban transportation, economics and finance. Hence, applying visualization methods to transform big data into effective information and knowledge is crucial for all industries.

In this Special Issue, we invite submissions exploring innovation and with a focus on advanced methodologies in big data collection and processing, data visualization analysis and related applications of big data and visualization, etc.

Dr. Yong Zhang
Prof. Dr. Yanming Shen
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 acquisition and pre-processing techniques
  • big data industry application
  • big data visual analytics and computing
  • visualisation of data handling and processing
  • visualisation design and systems
  • visualisation and visual analytics applications

Published Papers (6 papers)

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Research

17 pages, 12707 KiB  
Article
Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road
by Jiaxian Li, Yanyan Chen, Xiaoguang Yang and Ye Yuan
Appl. Sci. 2024, 14(5), 1780; https://doi.org/10.3390/app14051780 - 22 Feb 2024
Viewed by 352
Abstract
How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road [...] Read more.
How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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18 pages, 984 KiB  
Article
Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies
by Chien-Liang Chiu, Yensen Ni, Hung-Ching Hu, Min-Yuh Day and Yuhsin Chen
Appl. Sci. 2023, 13(23), 12805; https://doi.org/10.3390/app132312805 - 29 Nov 2023
Cited by 2 | Viewed by 902
Abstract
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not [...] Read more.
This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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15 pages, 3942 KiB  
Article
Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
by Kukjin Jang, Taegeon Song, Dasran Kim, Jinsick Kim, Byeongsoo Koo, Moonju Nam, Kyungil Kwak, Jooyeoun Lee and Myoungsug Chung
Appl. Sci. 2023, 13(21), 11800; https://doi.org/10.3390/app132111800 - 28 Oct 2023
Viewed by 722
Abstract
Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net [...] Read more.
Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net algorithm that utilizes images taken by unmanned vehicles. Unlike the U-Net algorithm, however, this algorithm identifies the optimal function by setting the epoch to 120 and uses the Adam optimization algorithm. In addition, a bilateral filter was applied to highlight the damaged areas of the bridge, and a different color was used for each of the five types of abnormalities detected, such as cracks. Next, we trained and tested 135,696 images of exterior bridge damage, including concrete delamination, water leakage, and exposed rebar. Through the analysis, we confirmed an analysis method that yields an average inspection reproduction rate of more than 95%. In addition, we compared and analyzed the inspection reproduction rate of the method with that of BIRD U-Net after using the same method and images for training as the existing U-Net and ResNet algorithms for validation. In addition, the algorithm developed in this study is expected to yield objective results through automatic damage analysis. It can be applied to regular inspections that involve unmanned mobile vehicles in the field of bridge maintenance, thereby reducing the associated time and cost. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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11 pages, 1378 KiB  
Article
Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge
by Ye Yuan, Jiang Chen, Hong Lang and Jian (John) Lu
Appl. Sci. 2023, 13(18), 9996; https://doi.org/10.3390/app13189996 - 05 Sep 2023
Viewed by 654
Abstract
Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of [...] Read more.
Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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12 pages, 1641 KiB  
Article
Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
by Jianxun Chen, Peng Chen and Xuxu Wu
Appl. Sci. 2023, 13(17), 9500; https://doi.org/10.3390/app13179500 - 22 Aug 2023
Viewed by 1374
Abstract
Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes [...] Read more.
Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes the content generation capability of ChatGPT to generate annotated data corpora for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, explicit entity markers and event knowledge are added to the text to construct generative input templates, enhancing the performance of event extraction. To validate the performance of this model, experiments are conducted on DuEE1.0 and Title2Event public datasets, and the results show that both data enhancement and prompt learning based on ChatGPT effectively improve the performance of the event extraction model, and the F1 values of the events extracted by the CPEE model proposed in this paper reach 85.1% and 59.9% on the two datasets, respectively, which are comparable to the existing models’ values of 1.3% and 10%, respectively; moreover, on the Title2Event dataset, the performance of different models on the event extraction task can be gradually improved as the data size of the annotated corpus of event extraction generated using ChatGPT increases. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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15 pages, 12287 KiB  
Article
Approximating Dynamic Elastic Modulus of Concrete for an Old Aqueduct Using Dynamic Tests and BP Neural Network
by Xiaobin Lu, Xiulin Li, Jun Xiao and Meng Li
Appl. Sci. 2023, 13(14), 8367; https://doi.org/10.3390/app13148367 - 19 Jul 2023
Viewed by 683
Abstract
Monitoring the degradation of the dynamic elastic modulus (Ed) of concrete is of great importance to track the durability deterioration for hydraulic concrete structures. For the aqueduct under investigation in this study, the dynamic elastic modulus of bent frames and [...] Read more.
Monitoring the degradation of the dynamic elastic modulus (Ed) of concrete is of great importance to track the durability deterioration for hydraulic concrete structures. For the aqueduct under investigation in this study, the dynamic elastic modulus of bent frames and moment frame supports (Ed-frame), the dynamic elastic modulus of arch trusses (Ed-arch) and the shear stiffnesses of the asphaltic bearings of U-shaped flumes (Kflume) are the main parameters to define the dynamic behavior of the structure, which have direct correlation with its vibrational characteristics and thus practicably can be estimated by a BP (back-propagation) neural network using modal frequencies as inputs. Since it is impossible to obtain sufficient experimental field data to train the network, a full-scale 3D FE model of the entire aqueduct is created, and modal analyses under different combinations of Kflume, Ed-arch and Ed-frame are conducted to generate the analytical dataset for the network. After the network’s architecture is refined by the cross-validation process and its modeling accuracy verified by the test procedure, the primary modal frequencies of the aqueduct obtained from in situ dynamic tests are put into the network to obtain the final approximations for Kflume, Ed-arch and Ed-frame, which sets an evaluation baseline of the general concrete Ed status for the aqueduct and indicates that the makeshift asphaltic bearings of U-shaped flumes basically can be treated as a three-directional hinge in the FE model. It is also found that more inputs of modal frequencies can improve the prediction accuracy of the BP neural network. Full article
(This article belongs to the Special Issue Advances in Big Data Analysis and Visualization)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Research on Relative Traffic Congestion Diagnosis Method for Urban Road
Author: Li Jiaxian
Highlights: Travel speed is an important index for evaluating urban road traffic congestion. However, the existing research lacks the research of urban road relative traffic congestion diagnosis method oriented to the user's travel experience. The paper took a typical intermittent flow urban trunk road (Taiping Street in Beijing) as the research object, constructed a diagnosis method combination of absolute traffic congestion and relative traffic congestion oriented to the user's travel experience, and carried out the diagnosis and analysis of absolute traffic congestion and relative traffic congestion on the urban road with the indexes of average travel speed, travel speed performance index, travel speed reduction rate, average travel delay per unit of distance, and traffic congestion incidence rate. The results showed that the traffic congestion was relatively serious in the south-north direction of Taiping Street during the morning peak hour, of which section 3 was the most serious traffic congestion during the morning peak hour. High traffic demand in the morning and evening rush hours, the increase in the traffic demand of students returning to school, and the decrease in the number of lanes on the road are the influencing factors of urban road traffic congestion. The research results can be applied to the actual urban road traffic congestion diagnosis and governance to provide support for improving the quality of urban road traffic travel.

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