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Applied Sciences
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Methods and Software for Big Data Analytics and Applications
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Methods and Software for Big Data Analytics 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: 20 September 2025 | Viewed by 1376

Special Issue Editor

Prof. Dr. Jiaxing Shang

E-Mail Website
Guest Editor
1. College of Computer Science, Chongqing University, Chongqing 400044, China
2. Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK
Interests: big data analytics; social network analysis and mining

Special Issue Information

Dear Colleagues,

In the era of Big Data, the ability to analyze and interpret vast volumes of information has become critical for driving innovation across various industries. This Special Issue focuses on the latest developments in methodologies and software tools that enable efficient data processing, analysis and visualization. It encompasses a wide range of topics, including machine learning algorithms, data mining techniques and cloud-based solutions that facilitate scalable analytics. The Special Issue also highlights novel applications in sectors such as healthcare, finance, safety and social media, where Big Data analytics is transforming decision-making processes. Contributions should address challenges related to data integration, security, analytics and real-time processing, offering insights into overcoming these hurdles. By showcasing cutting-edge research and practical implementations, this collection provides a comprehensive overview of the ways in which advanced analytical methods and innovative software are harnessing the potential of Big Data to deliver actionable insights. This Special Issue serves as a valuable resource for researchers, practitioners and policymakers seeking to understand and leverage the power of Big Data analytics to enhance organizational performance and societal outcomes. Submissions from scientists, academics and industrial practitioners across the world are welcome.

Prof. Dr. Jiaxing Shang
Guest Editor

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

  • AI and big data analytics
  • big data intelligence for various data-driven applications
  • computational intelligence methods for big data analytics
  • software tools for big data analytics
  • software engineering for big data applications
  • algorithms and methods for more efficient big data processing
  • methods and software for big data visualization
  • cloud-based solutions for scalable big data analytics
  • methods and software addressing big data safety and other open issues

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Published Papers (2 papers)

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Research

23 pages, 1811 KiB  
Article
EGA: An Efficient GPU Accelerated Groupby Aggregation Algorithm
by Zhe Wang, Yao Shen and Zhou Lei
Appl. Sci. 2025, 15(7), 3693; https://doi.org/10.3390/app15073693 - 27 Mar 2025
Viewed by 248
Abstract
With the exponential growth of big data, efficient groupby aggregation (GA) has become critical for real-time analytics across industries. GA is a key method for extracting valuable information. Current CPU-based solutions (such as large-scale parallel processing platforms) face computational throughput limitations. Since CPU-based [...] Read more.
With the exponential growth of big data, efficient groupby aggregation (GA) has become critical for real-time analytics across industries. GA is a key method for extracting valuable information. Current CPU-based solutions (such as large-scale parallel processing platforms) face computational throughput limitations. Since CPU-based platforms struggle to support real-time big data analysis, the GPU is introduced to support real-time GA analysis. Most GPU GA algorithms are based on hashing methods, and these algorithms experience performance degradation when the load factor of the hash table is too high or when the data volume exceeds the GPU memory capacity limit. This paper proposes an efficient hash-based GPU-accelerated groupby aggregation algorithm (EGA) that addresses these limitations. EGA features different designs for different scenarios: single-pass EGA (SP-EGA) maintains high efficiency when data fit in the GPU memory, while multipass EGA (MP-EGA) supports GA for data exceeding the GPU memory capacity. EGA demonstrates significant acceleration: SP-EGA outperforms SOTA hash-based GPU algorithms by 1.16–5.39× at load factors >0.90 and surpasses SOTA sort-based GPU methods by 1.30–2.48×. MP-EGA achieves 6.45–29.12× speedup over SOTA CPU implementations. Full article
(This article belongs to the Special Issue Methods and Software for Big Data Analytics and Applications)
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19 pages, 9959 KiB  
Article
Spatial–Temporal Reconstruction of Trajectories in Free Space Using Automatic Target Position Detection Data
by Yang Chen, Xin Chen, Bin Bai and Linjiang Zheng
Appl. Sci. 2024, 14(23), 11340; https://doi.org/10.3390/app142311340 - 5 Dec 2024
Viewed by 742
Abstract
The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by [...] Read more.
The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety. Full article
(This article belongs to the Special Issue Methods and Software for Big Data Analytics and Applications)
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