Special Issue "Big Data Analysis and Visualization Ⅱ"

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

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Prof. Dr. Kwan-Hee Yoo
Website
Guest Editor
Department of Computer Science, Chungbuk National University, 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea, 28644
Interests: big data analysis; data visualization; visual analytics; smart manufacturing; virtual reality; augmented reality
Special Issues and Collections in MDPI journals
Prof. Dr. Carson K. Leung
Website SciProfiles
Guest Editor
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: data mining and analysis; data visualization and visual analytics; health informatics and electronic health
Special Issues and Collections in MDPI journals
Prof. Dr. Nakhoon Baek
Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, Republic of Korea, 41566
Interests: big data processing; data visualization; massively parallel computing

Special Issue Information

Dear Colleagues,

Big data have become a core technology for providing innovative solutions in many fields. Big data analytics is a process of examining data to discover information, such as hidden patterns, unknown correlations, market insights, and customer preferences, that can be useful to make various business decisions. Recent advances in deep learning, machine learning, and data mining have improved to the point where these techniques can be used in analyzing big data in healthcare, manufacturing, social life, etc.

On the other hand, big data are being investigated using various visual analytical tools. These tools assist in visualizing new meanings and interpretations of the big data and, thus, can help better explore the data and simplify the complex big data analytics processes.

Hence, we invite the academic community and relevant industrial partners to submit papers to this Special Issue, on relevant fields and topics including (but not limited to) the following:

  • Novel algorithms for big data analysis
  • Big data preprocessing techniques (acquisition, integration, and cleaning)
  • Data mining, machine learning, and deep learning analysis for big data analysis
  • Application of computer vision techniques in big data analysis
  • Big database engineering and applications
  • Visual analytics of big database engineering and applications
  • Visualization and visual analytics for supporting the big data analysis process
  • Data structures for big data visualization
  • Application of big data visualization to a variety of fields
  • Big data visualization: case studies and applications

In addition to papers submitted by researchers, invited papers based on excellent contributions to recent conferences in this field will be included in this Special Issue; for example, from IDEAS 2020, IEEE CBDCom 2020, and BigDAS 2020.

Prof. Dr. Kwan-Hee Yoo
Prof. Dr. Carson K. Leung
Prof. Dr. Nakhoon Baek
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 papers will be 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 1800 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
  • Big data preprocessing
  • Big data analysis
  • Big data visualization
  • Visual analytics
  • Data mining
  • Machine learning
  • Deep learning
  • Computer vision
  • Multimedia big data

Published Papers (5 papers)

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Research

Open AccessArticle
Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
Appl. Sci. 2020, 10(21), 7413; https://doi.org/10.3390/app10217413 - 22 Oct 2020
Abstract
In modern manufacturing, the detection and prediction of machine anomalies, i.e., the inactive state of the machine during operation, is an important issue. Accurate inactive state detection models for factory machines can result in increased productivity. Moreover, they can guide engineers in implementing [...] Read more.
In modern manufacturing, the detection and prediction of machine anomalies, i.e., the inactive state of the machine during operation, is an important issue. Accurate inactive state detection models for factory machines can result in increased productivity. Moreover, they can guide engineers in implementing appropriate maintenance actions, which can prevent catastrophic failures and minimize economic losses. In this paper, we present a novel two-step data-driven method for the non-active detection of industry machines. First, we propose a feature extraction approach that aims to better distinguish the pattern of the active state and non-active state of the machine by multiple statistical analyses, such as reliability, time-domain, and frequency-domain analyses. Next, we construct a method to detect the active and non-active status of an industrial machine by applying various machine learning methods. The performance evaluation with a real-world dataset from the automobile part manufacturer demonstrates the proposed method achieves high accuracy. Full article
(This article belongs to the Special Issue Big Data Analysis and Visualization Ⅱ)
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Open AccessArticle
The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects
Appl. Sci. 2020, 10(19), 6946; https://doi.org/10.3390/app10196946 - 04 Oct 2020
Abstract
As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects [...] Read more.
As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems. Full article
(This article belongs to the Special Issue Big Data Analysis and Visualization Ⅱ)
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Open AccessArticle
Audio-Visual Tensor Fusion Network for Piano Player Posture Classification
Appl. Sci. 2020, 10(19), 6857; https://doi.org/10.3390/app10196857 - 29 Sep 2020
Abstract
Playing the piano in the correct position is important because the correct position helps to produce good sound and prevents injuries. Many studies have been conducted in the field of piano playing posture recognition that combines various techniques. Most of these techniques are [...] Read more.
Playing the piano in the correct position is important because the correct position helps to produce good sound and prevents injuries. Many studies have been conducted in the field of piano playing posture recognition that combines various techniques. Most of these techniques are based on analyzing visual information. However, in the piano education field, it is essential to utilize audio information in addition to visual information due to the deep relationship between posture and sound. In this paper, we propose an audio-visual tensor fusion network (simply, AV-TFN) for piano performance posture classification. Unlike existing studies that used only visual information, the proposed method uses audio information to improve the accuracy in classifying the postures of professional and amateur pianists. For this, we first introduce a dataset called C3Pap (Classic piano performance postures of amateur and professionals) that contains actual piano performance videos in diverse environments. Furthermore, we propose a data structure that represents audio-visual information. The proposed data structure represents audio information on the color scale and visual information on the black and white scale for representing relativeness between them. We call this data structure an audio-visual tensor. Finally, we compare the performance of the proposed method with state-of-the-art approaches: VN (Visual Network), AN (Audio Network), AVN (Audio-Visual Network) with concatenation and attention techniques. The experiment results demonstrate that AV-TFN outperforms existing studies and, thus, can be effectively used in the classification of piano playing postures. Full article
(This article belongs to the Special Issue Big Data Analysis and Visualization Ⅱ)
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Open AccessArticle
NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones
Appl. Sci. 2020, 10(19), 6657; https://doi.org/10.3390/app10196657 - 23 Sep 2020
Abstract
The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to [...] Read more.
The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to address the system resources to the correct application, making the system more intelligent and efficient. Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequences of data, outperforming all previous classification and prediction models. A recurrent neural network (RNN) is an artificial neural network associated with sequence models, and it can recognize patterns in sequences. One of the areas that use RNN is language modeling (LM). Given an arrangement of words, LM can learn how the words are organized in sentences, making it possible to predict the next word given a group of previous words. We propose building a predictive model inspired by LM. However, instead of using words, we will use previous applications to predict the next application. Moreover, some context features, such as timestamp and energy record, will be included in the prediction model to evaluate the impact of the features on the performance. We will provide the following application prediction result and extend it to the top-k possible candidates for the next application. Full article
(This article belongs to the Special Issue Big Data Analysis and Visualization Ⅱ)
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Open AccessArticle
Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
Appl. Sci. 2020, 10(16), 5705; https://doi.org/10.3390/app10165705 - 17 Aug 2020
Abstract
The location of user equipment (UE) should always be maintained in order to connect any incoming calls within a mobile network. While several methods of location registration have been proposed, most mobile networks have adopted zone-based registration due to its superior performance. Even [...] Read more.
The location of user equipment (UE) should always be maintained in order to connect any incoming calls within a mobile network. While several methods of location registration have been proposed, most mobile networks have adopted zone-based registration due to its superior performance. Even though recommendations from research on these zone-based systems state that multiple zones can be stored in a zone-based registration system, actual current mobile networks only employ a zone-based registration system that stores a single zone. Therefore, some studies have been conducted on zone-based registration using multiple zones. However, most of these studies consider only two zones. In this study, through the development of a semi-Markov process approach, we present a simple but accurate mathematical model for zone-based registration using three zones. In addition, our research results in zone-based registration systems where one, two and three zones are used to suggest the optimal management scheme for zone-based registration. Given that most mobile networks have already adopted some kind of zone-based registration, these results are able to directly enhance the performance of the actual mobile network in the near future with the minimum of effort required for implementation. Full article
(This article belongs to the Special Issue Big Data Analysis and Visualization Ⅱ)
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