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Applied Sciences
Special Issues
Recent Applications of Big Data Management and Analytics
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Recent Applications of Big Data Management and Analytics

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

Deadline for manuscript submissions: 10 May 2024 | Viewed by 918

Special Issue Editors

Prof. Dr. Talha Ali Khan

E-Mail Website
Guest Editor
Faculty of Tech and Software Engineering, University of Europe of Applied Sciences, 14469 Potsdam, Germany
Interests: artificial intelligence; data science; data classification; algorithms; optimization
Dr. Steve Ling

E-Mail Website
Guest Editor
Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 00099F, Australia
Interests: biomedical engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Academic interest in big data studies has grown substantially. However, decreasing the complexity of the data management system to extract optimal insights from large volumes of data is a constant management concern. In this context, the Special Issue on ‘Recent Applications of Big Data Management and Analytics’ of Applied Sciences encourages research at the intersection of strategic management and big data analytics to better understand how large amounts of data can be managed systematically and strategically to improve the practical implications of data for decision making, prediction, and classification using Artificial Intelligence methods. 

Prof. Dr. Talha Ali Khan
Dr. Steve Ling
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
  • data management
  • database
  • data engineering

Published Papers (1 paper)

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Research

23 pages, 6647 KiB  
Article
Name Disambiguation Scheme Based on Heterogeneous Academic Sites
by Dojin Choi, Junhyeok Jang, Sangho Song, Hyeonbyeong Lee, Jongtae Lim, Kyoungsoo Bok and Jaesoo Yoo
Appl. Sci. 2024, 14(1), 192; https://doi.org/10.3390/app14010192 - 25 Dec 2023
Viewed by 557
Abstract
Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher’s work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the [...] Read more.
Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher’s work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme for researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, we implement the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multi-classifier approach. We consider various researchers’ achievements and metadata of articles registered in various academic search sites. The proposed scheme showed an exceptionally high F1-measure value of 0.99. In this paper, we propose a multi-classifier that executes the most appropriate disambiguation scheme depending on the inputted metadata. The proposed multi-classifier shows the high F1-measure value of 0.67. Full article
(This article belongs to the Special Issue Recent Applications of Big Data Management and Analytics)
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