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4 pages, 147 KiB

Open AccessEditorial

Methods and Applications of Data Management and Analytics

by Wenjie Zhang and Zhengyi Yang

Appl. Sci. 2024, 14(24), 11637; https://doi.org/10.3390/app142411637 - 12 Dec 2024

Viewed by 1460

Abstract

In recent years, data management and analytics have attracted significant attention from both academia and industry, driven by the rapid growth in the volume, velocity, and variety of data generation [...] Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

Research

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16 pages, 4753 KiB

Open AccessArticle

Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery

by Carlos Cruz

Appl. Sci. 2024, 14(23), 11095; https://doi.org/10.3390/app142311095 - 28 Nov 2024

Viewed by 1453

Abstract

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a [...] Read more.

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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19 pages, 649 KiB

Open AccessArticle

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

by Jiaying Chen, Yiwen Cui, Xinguang Zhang, Jingyun Yang and Mengjie Zhou

Appl. Sci. 2024, 14(20), 9213; https://doi.org/10.3390/app14209213 - 10 Oct 2024

Cited by 5 | Viewed by 1437

Abstract

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional [...] Read more.

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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30 pages, 1597 KiB

Open AccessArticle

Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework

by Fernanda O. Gomes, Roberto Pellungrini, Anna Monreale, Chiara Renso and Jean E. Martina

Appl. Sci. 2024, 14(17), 8014; https://doi.org/10.3390/app14178014 - 7 Sep 2024

Cited by 2 | Viewed by 1009

Abstract

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background [...] Read more.

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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21 pages, 2375 KiB

Open AccessArticle

Mutation-Based Multivariate Time-Series Anomaly Generation on Latent Space with an Attention-Based Variational Recurrent Neural Network for Robust Anomaly Detection in an Industrial Control System

by Seungho Jeon, Kijong Koo, Daesung Moon and Jung Taek Seo

Appl. Sci. 2024, 14(17), 7714; https://doi.org/10.3390/app14177714 - 1 Sep 2024

Cited by 2 | Viewed by 2353

Abstract

Anomaly detection involves identifying data that deviates from normal patterns. Two primary strategies are used: one-class classification and binary classification. In Industrial Control Systems (ICS), where anomalies can cause significant damage, timely and accurate detection is essential, often requiring analysis of time-series data. [...] Read more.

Anomaly detection involves identifying data that deviates from normal patterns. Two primary strategies are used: one-class classification and binary classification. In Industrial Control Systems (ICS), where anomalies can cause significant damage, timely and accurate detection is essential, often requiring analysis of time-series data. One-class classification is commonly used but tends to have a high false alarm rate. To address this, binary classification is explored, which can better differentiate between normal and anomalous data, though it struggles with class imbalance in ICS datasets. This paper proposes a mutation-based technique for generating ICS time-series anomalies. The method maps ICS time-series data into a latent space using a variational recurrent autoencoder, applies mutation operations, and reconstructs the time-series, introducing plausible anomalies that reflect multivariate correlations. Evaluations of ICS datasets show that these synthetic anomalies are visually and statistically credible. Training a binary classifier on data augmented with these anomalies effectively mitigates the class imbalance problem. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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19 pages, 1199 KiB

Open AccessArticle

Product Demand Prediction with Spatial Graph Neural Networks

by Jiale Li, Li Fan, Xuran Wang, Tiejiang Sun and Mengjie Zhou

Appl. Sci. 2024, 14(16), 6989; https://doi.org/10.3390/app14166989 - 9 Aug 2024

Cited by 5 | Viewed by 3078

Abstract

In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature [...] Read more.

In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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21 pages, 3153 KiB

Open AccessArticle

Data Analytics for Optimizing and Predicting Employee Performance

by Laura Gabriela Tanasescu, Andreea Vines, Ana Ramona Bologa and Oana Vîrgolici

Appl. Sci. 2024, 14(8), 3254; https://doi.org/10.3390/app14083254 - 12 Apr 2024

Cited by 3 | Viewed by 6964

Abstract

The need to increase employee performance and productivity has become vital in most companies nowadays, considering the number of changes that processes and people have faced during recent years in many organizations. This becomes even more important as it can sustain the growth [...] Read more.

The need to increase employee performance and productivity has become vital in most companies nowadays, considering the number of changes that processes and people have faced during recent years in many organizations. This becomes even more important as it can sustain the growth of the company, as well as the competitiveness. This work will present multiple methods and comparisons between them for the process of building a machine learning algorithm to predict performance scores for employees in one organization; these methods include pre-processing the data, selecting the best variables, building the best algorithms for the available data, and tuning their hyperparameters. The current research aims to conclude on a collection of practices that will determine the best predictions for the given variables, so that human opinion can become less influential in employee appraisal, increasing objectivity and overall productivity. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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18 pages, 14737 KiB

Open AccessArticle

EEG Emotion Classification Based on Graph Convolutional Network

by Zhiqiang Fan, Fangyue Chen, Xiaokai Xia and Yu Liu

Appl. Sci. 2024, 14(2), 726; https://doi.org/10.3390/app14020726 - 15 Jan 2024

Cited by 9 | Viewed by 3151

Abstract

EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that [...] Read more.

EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overlooked in existing machine learning or deep learning models, which focus on individual channels in brain signals. Motivated by this, in this paper, we present RGNet, a model that is designed to learn the regional level representation of EEG signal for accurate emotion recognition. Specifically, after applying preprocessing and feature extraction techniques on raw signals, RGNet adopts a novel region-wise encoder to extract the features of channels located within each region as input to compute the regional level features, enabling the model to effectively explore the regional functionality. A graph is then constructed by considering each region as a node and connections between regions as edges, upon which a graph convolutional network is designed with spectral filtering and learned adjacency matrix. Instead of focusing on only the spatial proximity, it allows the model to capture more complex functional relationships. We conducted experiments from the perspective of region division strategies, region encoders and input feature types. Our model has achieved

98.64 %

and

99.33 %

for Deap and Dreamer datasets, respectively. The comparison studies show that RGNet outperforms the majority of the existing models for emotion recognition from EEG signals. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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22 pages, 3643 KiB

Open AccessArticle

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

by Jinping Yao, Yunhong Xu and Jiaojiao Gao

Appl. Sci. 2023, 13(22), 12305; https://doi.org/10.3390/app132212305 - 14 Nov 2023

Cited by 3 | Viewed by 2434

Abstract

With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter [...] Read more.

With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter a ‘cold start’ issue in the job recommendation process. Additionally, the nature of job recommendations, which differs fundamentally from unilateral recommendations, requires consideration of reciprocity between both parties involved. This article introduces a new approach to job recommendations using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students and solves the system’s cold-start problem using the social network to recommend jobs for college graduates where graduated students are employed. We introduce a dual-dimensional matching approach that incorporates both specialty and salary, building upon the amalgamated semantic keyword iterative algorithm and the social network job recommendation method, to enhance the reciprocity of job recommendations. The job recommendation method introduced herein outperforms other methods in terms of the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG), thereby confirming its superior ability to meet the job-seeking preferences of graduates and the recruitment criteria of employers. This job recommendation method offers effective assistance to graduates lacking employment experience and historical employment data, facilitating their search for more suitable job opportunities. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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30 pages, 15684 KiB

Open AccessArticle

Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering

by Neven Pičuljan and Željka Car

Appl. Sci. 2023, 13(10), 6234; https://doi.org/10.3390/app13106234 - 19 May 2023

Cited by 4 | Viewed by 3784

Abstract

In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong [...] Read more.

In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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24 pages, 1814 KiB

Open AccessArticle

Implicit Bias of Deep Learning in the Large Learning Rate Phase: A Data Separability Perspective

by Chunrui Liu, Wei Huang and Richard Yi Da Xu

Appl. Sci. 2023, 13(6), 3961; https://doi.org/10.3390/app13063961 - 20 Mar 2023

Cited by 4 | Viewed by 2558

Abstract

Previous literature on deep learning theory has focused on implicit bias with small learning rates. In this work, we explore the impact of data separability on the implicit bias of deep learning algorithms under the large learning rate. Using deep linear networks for [...] Read more.

Previous literature on deep learning theory has focused on implicit bias with small learning rates. In this work, we explore the impact of data separability on the implicit bias of deep learning algorithms under the large learning rate. Using deep linear networks for binary classification with the logistic loss under the large learning rate regime, we characterize the implicit bias effect with data separability on training dynamics. From a data analytics perspective, we claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the large learning rate phase, which results in improved generalization. Our theory is rigorously proven under the assumption of degenerate data by overcoming the difficulty of the non-constant Hessian of logistic loss and confirmed by experiments on both experimental and non-degenerated datasets. Our results highlight the importance of data separability in training dynamics and the benefits of learning rate annealing schemes using an initial large learning rate. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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36 pages, 8665 KiB

Open AccessReview

Machine Learning Methods in Weather and Climate Applications: A Survey

by Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang and Zhengyi Yang

Appl. Sci. 2023, 13(21), 12019; https://doi.org/10.3390/app132112019 - 3 Nov 2023

Cited by 63 | Viewed by 31889

Abstract

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of [...] Read more.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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33 pages, 1628 KiB

Open AccessReview

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

by Pu Chen, Linna Wu and Lei Wang

Appl. Sci. 2023, 13(18), 10258; https://doi.org/10.3390/app131810258 - 13 Sep 2023

Cited by 44 | Viewed by 39400

Abstract

This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and [...] Read more.

This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings. Full article

(This article belongs to the Special Issue Methods and Applications of Data Management and Analytics)

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