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Big Data: Analysis, Mining and Applications
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Big Data: Analysis, Mining and Applications

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4351

Special Issue Editors

Dr. Wen Zheng

E-Mail Website
Guest Editor
College of Data Sciences, Taiyuan University of Technology, Taiyuan 030024, China
Interests: big data technology; industrial Internet of Things; computational physics; impact dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Pengfei Wang

E-Mail Website
Guest Editor
Department of Modern Mechanics, University of Science and Technology of China, Chengdu 610056, China
Interests: multi-scale dynamics experimental technology; coupling mechanism and constitutive theory of material temperature/strain rate; dynamic properties of new fiber composite materials and their microstructure design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The "Big Data: Analysis, Mining, and Applications" Special Issue serves as a pivotal forum for scholars, researchers, and industry experts to delve into the forefront of advancements and practical applications within the domain of big data analytics and mining. In an era where data proliferation is unprecedented, the imperative for robust analytics and mining methodologies has never been more crucial. This Special Issue seeks to showcase innovative research, cutting-edge methodologies, and real-world applications that illuminate the diverse dimensions of big data analytics.

Against the backdrop of exponential data growth, the keywords encompass vital facets like machine learning integration, privacy considerations, real-time analytics, and scalability challenges. We invite contributions exploring novel algorithms, frameworks, and case studies across various sectors. As we navigate the evolving landscape of big data, this Special Issue aims to be a compass guiding the discourse on effective utilization, addressing challenges, and envisaging future trends in the dynamic field of big data analysis and mining. Researchers and practitioners are encouraged to submit their original work, fostering a collaborative environment that propels the understanding and harnessing of the vast potential within the realm of big data.

We invite submissions of original research articles, reviews, and case studies addressing, but not limited to, the following topics:

  1. Innovative Algorithms and Methods: We encourage submissions focusing on innovative algorithms and methods in the field of big data analysis and mining. Such research aims to advance methodological developments, enhancing efficiency and accuracy in handling large-scale data.
  2. Cross-Domain Applications: We welcome research exploring the applications of big data analytics across various domains such as healthcare, finance, and manufacturing, showcasing successful case studies, best practices, and solutions to industry-specific challenges.
  3. Integration of Machine Learning Techniques: We seek submissions on integrating machine learning techniques into big data analytics, aiming to improve the accuracy of predictive modeling, achieve more intelligent data analysis, and foster synergy between machine learning and big data.
  4. Emerging Trends and Future Directions: We encourage research on emerging trends and future directions in the field of big data analysis and mining. This includes exploring the forefront dynamics in technology, applications, and developmental trajectories within the field.

Dr. Wen Zheng
Dr. Pengfei Wang
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 analytics
  • data mining
  • artificial intelligence
  • machine learning
  • real-time analytics
  • privacy and security
  • application exploration

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Published Papers (3 papers)

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Research

29 pages, 4083 KiB  
Article
TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns
by Zhijie Huang and Jianfeng Liu
Appl. Sci. 2024, 14(15), 6611; https://doi.org/10.3390/app14156611 - 28 Jul 2024
Viewed by 1167
Abstract
Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the [...] Read more.
Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called “TransTLA”). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy. Full article
(This article belongs to the Special Issue Big Data: Analysis, Mining and Applications)
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17 pages, 6276 KiB  
Article
Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
by Bo Jiang, Xinyi Zhu, Xuecheng Tian, Wen Yi and Shuaian Wang
Appl. Sci. 2024, 14(15), 6414; https://doi.org/10.3390/app14156414 - 23 Jul 2024
Viewed by 1394
Abstract
In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased [...] Read more.
In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased challenges when multivariate feature vectors are handled. This paper introduces a novel prediction framework that integrates interpolation and extrapolation techniques. Central to this method are two main innovations: an optimization model that effectively classifies new multivariate data points as either interior or exterior to the known dataset, and a hybrid prediction system that combines k-nearest neighbor (kNN) and linear regression. Tested on the port state control (PSC) inspection dataset at the port of Hong Kong, our framework generally demonstrates superior precision in predictive outcomes than traditional kNN and linear regression models. This research enriches the literature by illustrating the enhanced capability of combining interpolation and extrapolation techniques in supervised learning. Full article
(This article belongs to the Special Issue Big Data: Analysis, Mining and Applications)
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17 pages, 804 KiB  
Article
Research and Application of an Improved Sparrow Search Algorithm
by Liwei Hu and Denghui Wang
Appl. Sci. 2024, 14(8), 3460; https://doi.org/10.3390/app14083460 - 19 Apr 2024
Cited by 2 | Viewed by 1001
Abstract
Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. [...] Read more.
Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm’s capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes. Full article
(This article belongs to the Special Issue Big Data: Analysis, Mining and Applications)
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