The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations.
{"title":"Product collaborative filtering based recommendation systems for large-scale E-commerce","authors":"Trang Trinh , Van-Ho Nguyen , Nghia Nguyen , Duy-Nghia Nguyen","doi":"10.1016/j.jjimei.2025.100322","DOIUrl":"10.1016/j.jjimei.2025.100322","url":null,"abstract":"<div><div>The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.jjimei.2025.100320
Maikel Lázaro Pérez Gort, Agostino Cortesi
Watermarking techniques aim to protect relational databases by embedding on them a copyright signal known as the watermark without imposing additional restrictions. However, unlike other digital assets, such as multimedia data, relational data are often subject to frequent updates that may dramatically compromise the quality of the embedded watermark. Hence, it is relevant to implement incremental watermarking for this type of data. Although incremental watermarking is defined in theory as the requirement of generating and inserting a mark whenever data is inserted or updated in a watermarked database (if the new value requires marking), its practical deployment is often ignored in the validation of proposed techniques, possibly due to how its deployment affects other requirements, such as the public system and security. In this work, we present different architectural approaches that, rather than conflicting with security and the public system, are built upon and contribute to them. The experimental results validate their applicability in terms of deployment, portability, scalability, and performance. As an architectural proposal, our work can be applied to different watermarking techniques, regardless of their particularities and the protected databases, making the preservation and enhancement of the watermark possible. Thus, we face the silent threats to security posed by opportunistic malicious operations in the absence of incremental watermarking.
{"title":"A robust scheme for securing relational data incremental watermarking","authors":"Maikel Lázaro Pérez Gort, Agostino Cortesi","doi":"10.1016/j.jjimei.2025.100320","DOIUrl":"10.1016/j.jjimei.2025.100320","url":null,"abstract":"<div><div>Watermarking techniques aim to protect relational databases by embedding on them a copyright signal known as the watermark without imposing additional restrictions. However, unlike other digital assets, such as multimedia data, relational data are often subject to frequent updates that may dramatically compromise the quality of the embedded watermark. Hence, it is relevant to implement incremental watermarking for this type of data. Although incremental watermarking is defined in theory as the requirement of generating and inserting a mark whenever data is inserted or updated in a watermarked database (if the new value requires marking), its practical deployment is often ignored in the validation of proposed techniques, possibly due to how its deployment affects other requirements, such as the public system and security. In this work, we present different architectural approaches that, rather than conflicting with security and the public system, are built upon and contribute to them. The experimental results validate their applicability in terms of deployment, portability, scalability, and performance. As an architectural proposal, our work can be applied to different watermarking techniques, regardless of their particularities and the protected databases, making the preservation and enhancement of the watermark possible. Thus, we face the silent threats to security posed by opportunistic malicious operations in the absence of incremental watermarking.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.jjimei.2025.100319
Berto Usman , Heris Rianto , Somnuk Aujirapongpan
This study examines how individual behavior is influenced by intentions and factors such as financial literacy, subjective norms, and perceived behavioral control. A quantitative survey was conducted with 263 respondents familiar with fintech applications, specifically digital payments, using purposive and snowball sampling technique. Empirical analysis with Smart-PLS reveals significant effects of these factors. Notably, financial literacy and perceived behavioral control significantly influence intention, whereas subjective norms shows no clear effect. Testing for indirect relationships indicates that intention serves as the sole mediator between perceived behavioral control and digital payment usage behavior. However, intention does not mediate the relationships between financial literacy and digital payment behavior or between subjective norms and digital payment behavior. This study's exploration of intention as a mediator provides valuable insights into the dynamics of these relationships, addressing a knowledge gap in management literature and contributing to the revisit of Theory of Planned Behavior in the context of digital payment adoption.
{"title":"Digital payment adoption: A revisit on the theory of planned behavior among the young generation","authors":"Berto Usman , Heris Rianto , Somnuk Aujirapongpan","doi":"10.1016/j.jjimei.2025.100319","DOIUrl":"10.1016/j.jjimei.2025.100319","url":null,"abstract":"<div><div>This study examines how individual behavior is influenced by intentions and factors such as financial literacy, subjective norms, and perceived behavioral control. A quantitative survey was conducted with 263 respondents familiar with fintech applications, specifically digital payments, using purposive and snowball sampling technique. Empirical analysis with Smart-PLS reveals significant effects of these factors. Notably, financial literacy and perceived behavioral control significantly influence intention, whereas subjective norms shows no clear effect. Testing for indirect relationships indicates that intention serves as the sole mediator between perceived behavioral control and digital payment usage behavior. However, intention does not mediate the relationships between financial literacy and digital payment behavior or between subjective norms and digital payment behavior. This study's exploration of intention as a mediator provides valuable insights into the dynamics of these relationships, addressing a knowledge gap in management literature and contributing to the revisit of Theory of Planned Behavior in the context of digital payment adoption.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We calculate sentiment from the Japanese Company Handbook, which contains a compact overview of Japanese companies’ business situation and financial data, using multiple methods, including large language models. Language models such as BERT and ChatGPT are advancing the application of natural language processing (NLP) to financial fields. We construct multiple sentiment calculation methods using sentiment dictionaries, models trained on existing sentiment datasets, ChatGPT, and GPT-4. Our analysis shows that stocks with higher sentiment scores tend to have higher excess returns, while those with lower scores tend to have lower excess returns. This feature is enhanced particularly in small-cap stocks. Comparisons between the models showed higher returns at high sentiment for the model trained with the existing sentiment dataset and lower returns at low sentiment for ChatGPT. The DeBERTaV2 model trained on Economy Watchers Survey data performed best in terms of returns at the highest sentiment quantile.
{"title":"Sentiment works in small-cap stocks: Japanese stock’s sentiment with language models","authors":"Masahiro Suzuki , Yasushi Ishikawa , Masayuki Teraguchi , Hiroki Sakaji","doi":"10.1016/j.jjimei.2024.100318","DOIUrl":"10.1016/j.jjimei.2024.100318","url":null,"abstract":"<div><div>We calculate sentiment from the Japanese Company Handbook, which contains a compact overview of Japanese companies’ business situation and financial data, using multiple methods, including large language models. Language models such as BERT and ChatGPT are advancing the application of natural language processing (NLP) to financial fields. We construct multiple sentiment calculation methods using sentiment dictionaries, models trained on existing sentiment datasets, ChatGPT, and GPT-4. Our analysis shows that stocks with higher sentiment scores tend to have higher excess returns, while those with lower scores tend to have lower excess returns. This feature is enhanced particularly in small-cap stocks. Comparisons between the models showed higher returns at high sentiment for the model trained with the existing sentiment dataset and lower returns at low sentiment for ChatGPT. The DeBERTaV2 model trained on Economy Watchers Survey data performed best in terms of returns at the highest sentiment quantile.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.jjimei.2024.100291
Gurdeep Singh
Smart technologies, specifically wearables are cutting edge innovation of design science with an emerging Artificial Intelligence (AI) capability for sustainable healthcare. Wearable IoT (w-IoT) applications, solutions and systems can promote early warning measures for physiological parameter monitoring and other vital health observation while addressing, streamlining and enhancing emergency response procedures in the provision and deliverance of healthcare services. These solutions exhibit real-time responses with underlying machine-learning (ML) methodologies alongside ubiquitous, context-aware, pervasive and advance software features. AI frameworks, for the development and implementation of solutions are well covered in this study adopting design science (DS) principles for new product development (NPD), comprising various healthcare scenarios for distributed numbers and environments. Physiological or health activity-related data produced by embedded optical smartwatch sensors can instigate sustainable and economical health-oriented solutions for continuous monitoring, semantic predictions for constrained, intractable and autonomous environments to address cardiac disorders. This paper addresses, the practical implementation of the w-IoT health technology solution prototype for real-time applicability, covering problem identification and utilizing design science guidelines, evaluation and contribution by emphasizing on the experimental stage in general and with specificity. It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. Machine Learning algorithms for classification with higher F-score or performance metrics adopted are Decision Trees (DT), K-Nearest Neighbours (KNN), XGboost, One-class SVM and Logistic Regression. In Binary classification, KNN achieved F-score of 91 %, followed by DT at 81 % which seems an effective algorithm with flexibility on overfitting with high accuracy result. This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs).
{"title":"Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare","authors":"Gurdeep Singh","doi":"10.1016/j.jjimei.2024.100291","DOIUrl":"10.1016/j.jjimei.2024.100291","url":null,"abstract":"<div><div>Smart technologies, specifically wearables are cutting edge innovation of design science with an emerging Artificial Intelligence (AI) capability for sustainable healthcare. Wearable IoT (w-IoT) applications, solutions and systems can promote early warning measures for physiological parameter monitoring and other vital health observation while addressing, streamlining and enhancing emergency response procedures in the provision and deliverance of healthcare services. These solutions exhibit real-time responses with underlying machine-learning (ML) methodologies alongside ubiquitous, context-aware, pervasive and advance software features. AI frameworks, for the development and implementation of solutions are well covered in this study adopting design science (DS) principles for new product development (NPD), comprising various healthcare scenarios for distributed numbers and environments. Physiological or health activity-related data produced by embedded optical smartwatch sensors can instigate sustainable and economical health-oriented solutions for continuous monitoring, semantic predictions for constrained, intractable and autonomous environments to address cardiac disorders. This paper addresses, the practical implementation of the w-IoT health technology solution prototype for real-time applicability, covering problem identification and utilizing design science guidelines, evaluation and contribution by emphasizing on the experimental stage in general and with specificity. It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. Machine Learning algorithms for classification with higher F-score or performance metrics adopted are Decision Trees (DT), K-Nearest Neighbours (KNN), XGboost, One-class SVM and Logistic Regression. In Binary classification, KNN achieved F-score of 91 %, followed by DT at 81 % which seems an effective algorithm with flexibility on overfitting with high accuracy result. This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs).</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100291"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.jjimei.2024.100311
Elizabeth Gómez , David Contreras , Ludovico Boratto , Maria Salamó
Going beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user preferences and demographic representation, which has been identified as a key aspect of fairness in recommender systems. In particular, provider fairness enables the generation of results which are equitable for different (groups of) providers. In this paper, we raise the problem of how recommendations are distributed when enabling provider fairness. Indeed, on the one hand, users have clear preferences with respect to which providers they choose (e.g., Italian users mostly buy Italian food), so recommendations should reflect these preferences. On the other hand, content providers should be able to reach a diverse audience, and be visible across the different user groups that expressed a preference for them. Specifically, we consider demographic groups based on their continent of origin for both users and providers, and assess how the preferences of the user groups are distributed across the provider groups. We first show that the state-of-the-art models and the existing approaches that enable provider fairness do not reflect the original distribution of the user preferences. To enable this property, we propose a re-ranking approach that, thanks to the use of buckets associating users and items, favors what we call preference distribution-aware provider fairness. Results on two real-world datasets (i.e., the Book-Crossing and COCO) show that our approach can enable provider fairness and tailor the recommendations to the original distribution of the user preferences, with negligible losses in effectiveness. In particular, in the Books dataset, our approach obtains an overall disparity that is around 6%. On the other hand, in the case of the COCO dataset, the disparities are reduced to 2%.
{"title":"Enhancing recommender systems with provider fairness through preference distribution awareness","authors":"Elizabeth Gómez , David Contreras , Ludovico Boratto , Maria Salamó","doi":"10.1016/j.jjimei.2024.100311","DOIUrl":"10.1016/j.jjimei.2024.100311","url":null,"abstract":"<div><div>Going beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user preferences and demographic representation, which has been identified as a key aspect of fairness in recommender systems. In particular, <em>provider fairness</em> enables the generation of results which are equitable for different (groups of) providers. In this paper, we raise the problem of <em>how recommendations are distributed when enabling provider fairness</em>. Indeed, on the one hand, users have clear preferences with respect to which providers they choose (<em>e.g.</em>, Italian users mostly buy Italian food), so recommendations should reflect these preferences. On the other hand, content providers should be able to reach a diverse audience, and be visible across the different user groups that expressed a preference for them. Specifically, we consider demographic groups based on their continent of origin for both users and providers, and assess how the preferences of the user groups are distributed across the provider groups. We first show that the state-of-the-art models and the existing approaches that enable provider fairness do not reflect the original distribution of the user preferences. To enable this property, we propose a re-ranking approach that, thanks to the use of buckets associating users and items, favors what we call <em>preference distribution-aware provider fairness</em>. Results on two real-world datasets (<em>i.e</em>., the Book-Crossing and COCO) show that our approach can enable provider fairness and tailor the recommendations to the original distribution of the user preferences, with negligible losses in effectiveness. In particular, in the Books dataset, our approach obtains an overall disparity that is around 6%. On the other hand, in the case of the COCO dataset, the disparities are reduced to 2%.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.jjimei.2024.100310
Matteo Francia, Enrico Gallinucci, Matteo Golfarelli
Materiality assessment is a critical process for companies to understand the interest perceived by its stakeholders towards topics related to environmental, social, and governance issues. Materiality assessment helps companies define their growth and communicative strategies; recently, it has become crucial within sustainability reporting, i.e., the practice of annually declaring the activities conducted to pursue economic growth in a sustainable way for society. In this paper, we propose a data-driven and automated approach to carry out materiality assessment. Stakeholders’ perception of important topics is obtained by analyzing relevant textual documents (e.g., company reports, press releases, social media posts), identifying mentions of potentially interesting topics, and converting them to scores that produce materiality rankings or matrices. An iterative methodology is proposed to incrementally carry out materiality assessment by progressively building the domain knowledge required to automate the process. Efficiency and effectiveness evaluations are carried out in a real-world scenario.
{"title":"Automating materiality assessment with a data-driven document-based approach","authors":"Matteo Francia, Enrico Gallinucci, Matteo Golfarelli","doi":"10.1016/j.jjimei.2024.100310","DOIUrl":"10.1016/j.jjimei.2024.100310","url":null,"abstract":"<div><div>Materiality assessment is a critical process for companies to understand the interest perceived by its stakeholders towards topics related to environmental, social, and governance issues. Materiality assessment helps companies define their growth and communicative strategies; recently, it has become crucial within sustainability reporting, i.e., the practice of annually declaring the activities conducted to pursue economic growth in a sustainable way for society. In this paper, we propose a data-driven and automated approach to carry out materiality assessment. Stakeholders’ perception of important topics is obtained by analyzing relevant textual documents (e.g., company reports, press releases, social media posts), identifying mentions of potentially interesting topics, and converting them to scores that produce materiality rankings or matrices. An iterative methodology is proposed to incrementally carry out materiality assessment by progressively building the domain knowledge required to automate the process. Efficiency and effectiveness evaluations are carried out in a real-world scenario.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-23DOI: 10.1016/j.jjimei.2024.100314
Umar Ali Bukar , Md Shohel Sayeed , Oluwatosin Ahmed Amodu , Siti Fatimah Abdul Razak , Sumendra Yogarayan , Mohamed Othman
Analysing social media data is crucial for crisis management organisations to make timely decisions. Researchers in crisis informatics have devised various methods and systems to process and classify large volumes of crisis-related social media data for effective crisis response and recovery. However, the complexity of previous solutions hampers the timely processing of this data, its visualisation, and its interpretation, which is necessary for effective crisis response. Hence, this study addresses this challenge by employing visualisation of similarities to analyse and visualise crisis datasets to aid crisis management and decision-making. The results reveal a "nine-cluster community” of relevant keywords comprising “Green, Brown, Red, Blue, Pink, Purple, Yellow, Orange, and Cyan” colours, in both binary and full count. Specifically, the findings reveal various keywords such as the needs for food, water, shelter, medicine, and electricity. Thereafter, the study discusses the implications of VOSviewer for analysing crisis data theoretically and practically.
{"title":"Leveraging VOSviewer approach for mapping, visualisation, and interpretation of crisis data for disaster management and decision-making","authors":"Umar Ali Bukar , Md Shohel Sayeed , Oluwatosin Ahmed Amodu , Siti Fatimah Abdul Razak , Sumendra Yogarayan , Mohamed Othman","doi":"10.1016/j.jjimei.2024.100314","DOIUrl":"10.1016/j.jjimei.2024.100314","url":null,"abstract":"<div><div>Analysing social media data is crucial for crisis management organisations to make timely decisions. Researchers in crisis informatics have devised various methods and systems to process and classify large volumes of crisis-related social media data for effective crisis response and recovery. However, the complexity of previous solutions hampers the timely processing of this data, its visualisation, and its interpretation, which is necessary for effective crisis response. Hence, this study addresses this challenge by employing <em>visualisation of similarities</em> to analyse and visualise crisis datasets to aid crisis management and decision-making. The results reveal a \"nine-cluster community” of relevant keywords comprising “Green, Brown, Red, Blue, Pink, Purple, Yellow, Orange, and Cyan” colours, in both binary and full count. Specifically, the findings reveal various keywords such as the needs for food, water, shelter, medicine, and electricity. Thereafter, the study discusses the implications of VOSviewer for analysing crisis data theoretically and practically.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-21DOI: 10.1016/j.jjimei.2024.100316
Stav Cohen , Barak Fishbain
The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the "SunflowerLand" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.
{"title":"Advancing Metaverse's experience through optimization of players’ decisions","authors":"Stav Cohen , Barak Fishbain","doi":"10.1016/j.jjimei.2024.100316","DOIUrl":"10.1016/j.jjimei.2024.100316","url":null,"abstract":"<div><div>The Metaverse, a virtual world enabling user interaction with digital environments and assets, increasingly utilizes blockchain and Non-Fungible Tokens (NFTs) to represent unique characters and items. Representing and valuing individual digital characters is crucial in the Metaverse as it establishes ownership, identity, and status within these virtual worlds. This research investigates decision-making within collaborative online games, where players face the challenge of optimizing their character's value through strategic NFT acquisition. This challenge is analogous to the Knapsack Problem, aiming to maximize the value of items selected within a limited capacity, just as players seek to maximize character rarity within budget constraints. We propose a Decision Support System (DSS) employing genetic algorithms to assist players in tackling this complex optimization problem. A simulation framework, based on the \"SunflowerLand\" Metaverse, demonstrates that players using the DSS achieve significantly higher character rarity, highlighting the potential of this approach to enhance player experience.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1016/j.jjimei.2024.100315
Ramanpreet Kaur, Tomaž Klobučar, Dušan Gabrijelčič
Language models are transforming cybersecurity by addressing critical challenges such as the growing skills gap, the need for expertise augmentation, and knowledge retention. These models offer scalable, adaptable, and round-the-clock defenses against evolving cyber threats. By generating human-like text, processing data efficiently, and providing actionable responses, language models bridge the gap between automated systems and human expertise for different cybersecurity applications. However, the application and adaptation of language models for cyber security is still in its infancy. This review explores the use of general models, such as BERT, and larger models in cybersecurity research. It provides a structured framework for developing customized language models tailored to applications including content analysis, software and systems analysis, threat intelligence and monitoring, and cyber vetting. The study critically examines challenges, such as data confidentiality, infrastructure requirements, integration complexity and the evolving threat landscape. Moreover, it underscores the need for transparency, responsible use, and bias mitigation to ensure reliable and secure deployment of these models. In addition, this work critically examines the socio-technical dimensions of language model integration, focusing on their impact on organizational workflows, decision making and human-machine collaboration. By considering both technical and socio-technical considerations, this review provides a roadmap for future research and development. It highlights the potential of language models to improve organizational resilience, ensure secure implementation, and support informed decision-making in cybersecurity practice.
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