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Applied Sciences
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Application Research in Big Data Technologies
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Application Research in Big Data Technologies

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 September 2023) | Viewed by 4390

Special Issue Editors

Prof. Dr. Jason J. Jung

E-Mail Website
Guest Editor
Department of Computer Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: artificial intelligence; big data; social networks; story analytics; story engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Gen Li

E-Mail Website
Guest Editor
Department of Computer Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: artificial intelligence; anomaly detection; big data

Special Issue Information

Dear colleagues,

This Special Issue aims to present research papers that apply novel big data technologies in a variety of areas.

With the rapid growth of data, big data have attracted the attention of researchers. These huge data are extremely useful and valuable for scientific exploration, improving business productivity and human progress. Big data technologies have been widely applied in a variety of areas, such as social science, finance, health care, the environment and climate. In addition, with the development of big data technologies, many applications have also been developed, such as recommendation systems and anomaly detection. 

I thus invite you to submit your innovative and high-quality contributions in the form of original research papers to show the progress made in these areas. The topics for this Special Issue include, but are not restricted to, big data analytics, big data visualization, novel theory, novel algorithms, and big data technology-based applications

Prof. Dr. Jason J. Jung
Dr. Gen Li
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 applications
  • deep learning applications
  • social network services and applications
  • recommendation system

Published Papers (2 papers)

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Research

20 pages, 632 KiB  
Article
Smart Mobility with Big Data: Approaches, Applications, and Challenges
by Dohoon Lee, David Camacho and Jason J. Jung
Appl. Sci. 2023, 13(12), 7244; https://doi.org/10.3390/app13127244 - 17 Jun 2023
Cited by 2 | Viewed by 2358
Abstract
Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to [...] Read more.
Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed. Full article
(This article belongs to the Special Issue Application Research in Big Data Technologies)
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19 pages, 5007 KiB  
Article
Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
by Mengzhao Zhang, Qian Tang, Jeong-Geun Kim, Bernd Burgstaller and Shin-Dug Kim
Appl. Sci. 2023, 13(7), 4436; https://doi.org/10.3390/app13074436 - 31 Mar 2023
Viewed by 1222
Abstract
This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big [...] Read more.
This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models. Full article
(This article belongs to the Special Issue Application Research in Big Data Technologies)
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