Big Data Intelligence: Methodologies and Applications

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 Proposed submissions should be original, unpublished, and novel for in-depth research.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

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

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All BDCC Data MAKE Mathematics

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

14 pages, 852 KiB  

Open AccessArticle

Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation

by Xuecheng Tian, Bo Jiang, King-Wah Pang, Yu Guo, Yong Jin and Shuaian Wang

Mathematics 2024, 12(11), 1612; https://doi.org/10.3390/math12111612 - 21 May 2024

Viewed by 278

Abstract

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional [...] Read more.

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems. Full article

(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)

46 pages, 3360 KiB  

Open AccessReview

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

by Maya Cendana and Ren-Jieh Kuo

Mach. Learn. Knowl. Extr. 2024, 6(2), 1009-1054; https://doi.org/10.3390/make6020047 - 7 May 2024

Viewed by 1049

Abstract

Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have [...] Read more.

Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering. Full article

(This article belongs to the Topic Big Data Intelligence: Methodologies and Applications)

►▼ Show Figures

Figure 1

11 pages, 1606 KiB  

Open AccessArticle

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 - 3 Apr 2024

Viewed by 1061

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)

►▼ Show Figures

Figure 1

20 pages, 1028 KiB  

Open AccessReview

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 1721

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)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-4