Data Science and Management最新文献

{"title":"Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning","authors":"Abhishek Bajpai ,&nbsp;Harshita Verma ,&nbsp;Anita Yadav","doi":"10.1016/j.dsm.2024.02.001","DOIUrl":"10.1016/j.dsm.2024.02.001","url":null,"abstract":"<div><p>The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000110/pdfft?md5=e7e054f24c3ef64041af32bb112d9eb3&pid=1-s2.0-S2666764924000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139881740","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}

{"title":"Original optimal method to solve the all-pairs shortest path problem: Dhouib-matrix-ALL-SPP","authors":"Souhail Dhouib","doi":"10.1016/j.dsm.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2024.01.005","url":null,"abstract":"<div><p>The All-pairs shortest path problem (ALL-SPP) aims to find the shortest path joining all the vertices in a given graph. This study proposed a new optimal method, Dhouib-matrix-ALL-SPP (DM-ALL-SPP) to solve the ALL-SPP based on column-row navigation through the adjacency matrix. DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges. Even for graphs with a negative cycle, DM-ALL-SPP reported a negative cycle. In addition, DM-ALL-SPP continues to work for directed, undirected and mixed graphs. Furthermore, it is characterized by two phases: the first phase consists of adding by column repeated (<em>n</em>) iterations (where <em>n</em> is the number of vertices), and the second phase resides in adding by row executed in the worst case <em>(n∗log(n))</em> iterations. The first phase, focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value. The second phase is emphasized by rows only for the elements modified in the first phase. Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method, which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000109/pdfft?md5=d538a0a331fded270406098b5f8fd6f2&pid=1-s2.0-S2666764924000109-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243490","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}

{"title":"Engaging in sports via the metaverse? an examination through analysis of metaverse research trends in sports","authors":"Ahyun Kim ,&nbsp;Sang-Soo Kim","doi":"10.1016/j.dsm.2024.01.002","DOIUrl":"10.1016/j.dsm.2024.01.002","url":null,"abstract":"<div><p>In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse,” “augmented reality,” “virtual reality,” “lifelogging,” and “mixed reality.” First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog (y/ies)”,“experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious; however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266676492400002X/pdfft?md5=116807c54c64af0697387e036e1948c7&pid=1-s2.0-S266676492400002X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633801","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}

{"title":"Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology","authors":"Taqwa Hariguna ,&nbsp;Athapol Ruangkanjanases","doi":"10.1016/j.dsm.2024.01.001","DOIUrl":"10.1016/j.dsm.2024.01.001","url":null,"abstract":"<div><p>The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000018/pdfft?md5=ff997f9e6eeea260084310750d46c9aa&pid=1-s2.0-S2666764924000018-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634853","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}

{"title":"The relationship between attribute performance and customer satisfaction: An interpretable machine learning approach","authors":"Jie Wang ,&nbsp;Jing Wu ,&nbsp;Shaolong Sun ,&nbsp;Shouyang Wang","doi":"10.1016/j.dsm.2024.01.003","DOIUrl":"10.1016/j.dsm.2024.01.003","url":null,"abstract":"<div><p>Understanding the relationship between attribute performance (AP) and customer satisfaction (CS) is crucial for the hospitality industry. However, accurately modeling this relationship remains challenging. To address this issue, we propose an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) approach that leverages interpretable machine learning (IML) to improve traditional relationship analysis methods. The IML-DAA employs extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to construct relationships and explain the significance of each attribute. Following this, an improved version of penalty-reward contrast analysis (PRCA) is used to classify attributes, whereas asymmetric impact-performance analysis (AIPA) is employed to determine the attribute improvement priority order. A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated. The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS, as identified by the dynamic AIPA (DAIPA) model. This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000031/pdfft?md5=f340fae10be77a7b1b0ac97f65b1003c&pid=1-s2.0-S2666764924000031-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540135","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}

{"title":"Metaverse application, flow experience, and Gen-Zers’ participation intention of intangible cultural heritage communication","authors":"Yuhua Cao ,&nbsp;Xiaoli Qu ,&nbsp;Xiangfen Chen","doi":"10.1016/j.dsm.2023.12.004","DOIUrl":"10.1016/j.dsm.2023.12.004","url":null,"abstract":"<div><p>With outstanding advantages in virtuality, immersion, connectivity, and openness, the application of the Metaverse in the development of intangible cultural heritage has demonstrated great potential to enhance Gen-Zers’ participation intention, but the effect and its mechanism remain unclear. This study constructs a theoretical model based on the stimuli-organism-response (SOR) theory, the DeLone and McLean model of information system (IS) success (D&amp;M) model, and flow theory, and conducts an empirical study using a structural equation model and regression analysis based on questionnaire survey data in China to uncover whether and how Metaverse application exerts its impact. Results show that Metaverse application can enhance Gen-Zers’ participation intention in the communication of intangible cultural heritage, and their mechanism follows a chain path of “stimulus-state-response” under the joint action of “technology-individual-environment” in which Metaverse application is the key stimulus factor, flow experience is the mediator, and self-efficacy and subjective norm are moderators. The findings can offer new insights for research on Metaverse application from the perspectives of consequences and effects and can also provide practical implications for Metaverse application as well as the development and communication of intangible cultural heritage.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000619/pdfft?md5=a4682285b334aaec5904fc1c4ff07288&pid=1-s2.0-S2666764923000619-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139126072","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}

{"title":"Topic prevalence and trends of metaverse in healthcare: A bibliometric analysis","authors":"Pei Wu ,&nbsp;Donghua Chen ,&nbsp;Runtong Zhang","doi":"10.1016/j.dsm.2023.12.003","DOIUrl":"10.1016/j.dsm.2023.12.003","url":null,"abstract":"<div><p>Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare; in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000607/pdfft?md5=4a86705d550b96068560f4c1c4c8bbee&pid=1-s2.0-S2666764923000607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195946","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}

{"title":"Secure approach to sharing digitized medical data in a cloud environment","authors":"Kukatlapalli Pradeep Kumar ,&nbsp;Boppuru Rudra Prathap ,&nbsp;Michael Moses Thiruthuvanathan ,&nbsp;Hari Murthy ,&nbsp;Vinay Jha Pillai","doi":"10.1016/j.dsm.2023.12.001","DOIUrl":"10.1016/j.dsm.2023.12.001","url":null,"abstract":"<div><p>Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000589/pdfft?md5=7be31440af8c8ec0561c9c1630c7620b&pid=1-s2.0-S2666764923000589-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139018223","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}

{"title":"Divide and recombine approach for warranty database: Estimating the reliability of an automobile component","authors":"Md Rezaul Karim","doi":"10.1016/j.dsm.2023.12.002","DOIUrl":"10.1016/j.dsm.2023.12.002","url":null,"abstract":"<div><p>The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&amp;R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&amp;R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&amp;R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000590/pdfft?md5=b2090e0cb2adf29a7859934cfe00ea3e&pid=1-s2.0-S2666764923000590-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013440","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}

{"title":"Data- and management-driven metaverse research","authors":"Zhigeng Pan ,&nbsp;Jiaqi Yan ,&nbsp;Hirotoshi Takeda ,&nbsp;Haibing Lu ,&nbsp;Shan Liu ,&nbsp;Wei Huang ,&nbsp;Jian Mou ,&nbsp;James Christopher Westland","doi":"10.1016/j.dsm.2023.11.003","DOIUrl":"10.1016/j.dsm.2023.11.003","url":null,"abstract":"<div><p>The metaverse has become a very important phenomenon in society because of the emergence of new technologies. The widespread adoption of the metaverse has generated significant discussions about the challenges and opportunities it presents. We invited three panelists to present their personal viewpoints on the metaverse in the 2022 AIS-SIG-ISAP Workshop on Information Systems in Asia-Pacific (ISAP). The discussion indicated that metaverse research is being conducted. Furthermore, it highlighted new research directions and offered research topics related to the advantages or disadvantages of the metaverse. The proposed research topics will offer new insights to academics and practitioners.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000528/pdfft?md5=6125369c75ab32b2ac7de63a7009367c&pid=1-s2.0-S2666764923000528-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139299444","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}