Topic Editors

School of Software Technology, Dalian University of Technology, Dalian, China
School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Dr. Boxiang Dong
Department of Computer Science, Montclair State University, Montclair, NJ, USA

Big Data Intelligence: Methodologies and Applications

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
Viewed by
2009

Topic Information

Dear Colleagues,

In the big data era, with the enrichment of data collection and description measures, a wide array of data in various formats are collected much easier than before. It is significant to discover the knowledge hidden in the mass by comprehensive understanding and learning to realize the data intelligence, which can help human in various dimensions, such as intelligent decisions and predictive services. However, the high-dimensional, heterogeneous, real-time, and low-quality characteristics of the collected data pose great challenges to the design of knowledge discovery methods. If we can effectively perform feature learning on massive high-dimensional, heterogeneous, real-time, and low-quality big data to discover the hidden knowledge and rules, the potential values and insights can be identified. Thus, it will provide a comprehensive understanding and a favorable decision-making framework based on the massive data to realize the real big data intelligence.

This topic aims to seek the high-quality papers from academics and industry-related researchers in the areas of big data, data mining, machine learning, artificial intelligence, and multimedia analysis to present the most recently advanced methods and applications for realizing big data intelligence. Proposed submissions should be original, unpublished, and novel for in-depth research. Topics include but not limited to:

  • Big Data Theory and Methods;
  • Artificial Intelligence Theory and Methods;
  • Multimodal Data Analysis;
  • Domain Adaption and Transfer Learning;
  • Deep Learning and Reinforcement Learning;
  • Knowledge Graphs;
  • Natural Language Processing;
  • Cross-modal Index;
  • Uncertainty Data Analysis;
  • Data Reliability Analysis;
  • Medical Big Data Analysis and Application;
  • Industrial Big Data Analysis and Application;
  • Big data Analysis and Application in Other Fields.

Prof. Dr. Liang Zhao
Dr. Liang Zou
Dr. Boxiang Dong
Topic Editors

Keywords

  • big data
  • artificial intelligence
  • multimodal learning
  • knowledge graphs
  • data reliability

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
BDCC
3.7 4.9 2017 18.2 Days CHF 1800 Submit
Data
data
2.6 4.6 2016 22 Days CHF 1600 Submit
Machine Learning and Knowledge Extraction
make
3.9 8.5 2019 19.9 Days CHF 1800 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit

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Published Papers (2 papers)

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11 pages, 1606 KiB  
Article
Effective Data Reduction Using Discriminative Feature Selection Based on Principal Component Analysis
by Faith Nwokoma, Justin Foreman and Cajetan M. Akujuobi
Mach. Learn. Knowl. Extr. 2024, 6(2), 789-799; https://doi.org/10.3390/make6020037 - 03 Apr 2024
Viewed by 563
Abstract
Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used [...] Read more.
Effective data reduction must retain the greatest possible amount of informative content of the data under examination. Feature selection is the default for dimensionality reduction, as the relevant features of a dataset are usually retained through this method. In this study, we used unsupervised learning to discover the top-k discriminative features present in the large multivariate IoT dataset used. We used the statistics of principal component analysis to filter the relevant features based on the ranks of the features along the principal directions while also considering the coefficients of the components. The selected number of principal components was used to decide the number of features to be selected in the SVD process. A number of experiments were conducted using different benchmark datasets, and the effectiveness of the proposed method was evaluated based on the reconstruction error. The potency of the results was verified by subjecting the algorithm to a large IoT dataset, and we compared the performance based on accuracy and reconstruction error to the results of the benchmark datasets. The performance evaluation showed consistency with the results obtained with the benchmark datasets, which were of high accuracy and low reconstruction error. Full article
(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)
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20 pages, 1028 KiB  
Review
Luxury Car Data Analysis: A Literature Review
by Pegah Barakati, Flavio Bertini, Emanuele Corsi, Maurizio Gabbrielli and Danilo Montesi
Data 2024, 9(4), 48; https://doi.org/10.3390/data9040048 - 30 Mar 2024
Viewed by 969
Abstract
The concept of luxury, considering it a rare and exclusive attribute, is evolving due to technological advances and the increasing influence of consumers in the market. Luxury cars have always symbolized wealth, social status, and sophistication. Recently, as technology progresses, the ability and [...] Read more.
The concept of luxury, considering it a rare and exclusive attribute, is evolving due to technological advances and the increasing influence of consumers in the market. Luxury cars have always symbolized wealth, social status, and sophistication. Recently, as technology progresses, the ability and interest to gather, store, and analyze data from these elegant vehicles has also increased. In recent years, the analysis of luxury car data has emerged as a significant area of research, highlighting researchers’ exploration of various aspects that may differentiate luxury cars from ordinary ones. For instance, researchers study factors such as economic impact, technological advancements, customer preferences and demographics, environmental implications, brand reputation, security, and performance. Although the percentage of individuals purchasing luxury cars is lower than that of ordinary cars, the significance of analyzing luxury car data lies in its impact on various aspects of the automotive industry and society. This literature review aims to provide an overview of the current state of the art in luxury car data analysis. Full article
(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)
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