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 5562
Special Issue Editors
Interests: big data analysis; artificial intelligence; intelligent transportation; graphics and images
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
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.