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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: closed (10 May 2024) | Viewed by 1637

Special Issue Editors


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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

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

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Keywords

  • big data
  • data management
  • database
  • data engineering

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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 1044
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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