Data Science and Management最新文献

{"title":"When cryptography stops data science: Strategies for resolving the conflicts between data scientists and cryptographers","authors":"","doi":"10.1016/j.dsm.2024.03.001","DOIUrl":"10.1016/j.dsm.2024.03.001","url":null,"abstract":"<div><p>The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 238-255"},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000134/pdfft?md5=74cffc92910a646ae465235dd70aec61&pid=1-s2.0-S2666764924000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140269093","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":"Survival strategies for family-run homestays: Analyzing user reviews through text mining","authors":"Jay Krishnan ,&nbsp;Biplab Bhattacharjee ,&nbsp;Maheshwar Pratap ,&nbsp;Janardan Krishna Yadav ,&nbsp;Moinak Maiti","doi":"10.1016/j.dsm.2024.03.003","DOIUrl":"10.1016/j.dsm.2024.03.003","url":null,"abstract":"<div><p>Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located in four thematic areas of Kerala. Accordingly, various techniques have been deployed, such as sentiment and emotional analyses, topic modeling, and clustering methods. Key themes revealed from topic modeling were breakfast, facilities provided, ambience, bathroom, cleanliness, hospitality exhibited, and satisfaction with the host. A lasso logistic regression-based predictive binary text classification model (with 97.6% accuracy) for homestay recommendations was developed. Our findings and predictive model have implications for managers and homestay owners in devising appropriate marketing strategies and improving their overall guest experience.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 228-237"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000158/pdfft?md5=89c56a3dcbb307fb2011d2afb14b790b&pid=1-s2.0-S2666764924000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990805","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":"Development of a machine learning model for predicting abnormalities of commercial airplanes","authors":"","doi":"10.1016/j.dsm.2024.03.002","DOIUrl":"10.1016/j.dsm.2024.03.002","url":null,"abstract":"<div><p>Airplanes are a social necessity for movement of humans, goods, and other. They are generally safe modes of transportation; however, incidents and accidents occasionally occur. To prevent aviation accidents, it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly detection, and abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection and labeling, prediction-model development, deployment, and testing. The data labeling process was based on the rules framed by the international civil aviation organization for commercial, jet-engine flights and validated by expert commercial pilots. The results showed that the best prediction model, the quadratic-discriminant-analysis, was 93% accurate, indicating a “good fit”. Moreover, the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96, respectively, thus confirming its “good fit”.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 256-265"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000146/pdfft?md5=72d22c62c77d91a47de3980ce379bce3&pid=1-s2.0-S2666764924000146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140270485","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":"Machine acceleration time series prediction for dimensional accuracy of 3D printed parts","authors":"","doi":"10.1016/j.dsm.2024.02.002","DOIUrl":"10.1016/j.dsm.2024.02.002","url":null,"abstract":"<div><p>This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional (3D) printing, particularly focusing on extrusion technology. Our primary objective was to develop a long short-term memory (LSTM) network capable of assessing this impact. We conducted an extensive analysis involving 12 distinct infill patterns, collecting time-series data to examine their effects on the acceleration of the printer’s bed. The LSTM network was trained using acceleration data from the adaptive cubic infill pattern, while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy. This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model. Specifically, the LSTM model was devised to predict the acceleration of a fused deposition modeling (FDM) printer using data from the adaptive cubic infill pattern. Rigorous testing yielded a root mean square error (RMSE) of 0.007144, reflecting the model’s precision. Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern, resulting in an RMSE of 0.007328. Notably, the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network (RNN) in predicting machine acceleration data. The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 218-227"},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000122/pdfft?md5=5279d6a024ea6a759468bdafb34bcc56&pid=1-s2.0-S2666764924000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463746","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":"Determinants of continuous usage intention of branded apps in omni-channel retail environment: Comparison between experience-oriented and transaction-oriented apps","authors":"Lixia Jiang,&nbsp;Shenglan Yang,&nbsp;Qing Tang,&nbsp;Zhengjie Zhang","doi":"10.1016/j.dsm.2024.01.004","DOIUrl":"10.1016/j.dsm.2024.01.004","url":null,"abstract":"<div><p>Branded applications (apps) have become core touchpoints for improving consumer shopping experiences in omni-channel retailing, and many firms have developed different types of branded apps to provide additional value. Moreover, continuous usage intention is the key to improving enterprises’ gain efficiency and consumers’ brand loyalty. This study aims to reveal how branded apps achieve continuance intention from the perspective of consumer perceptions by combining the technology acceptance model and investigating the impact of differences in channel features on usage behavior between the two types of branded apps. An experiment was designed comparing transaction- and experience-oriented branded apps. A structural equation modeling technique was employed to validate the model based on the survey data of respondents from the experimental groups. The results show that the supportive role of omni-channel has a unique experience mechanism that promotes continuous usage intention. However, there are two discrepant results regarding the effect of perceived complementarity on perceived usefulness in transaction- and experience-oriented branded apps. The mediating role of perceived usefulness between perceived consistency, complementarity, ease of use and consumer satisfaction was supported in the experience-oriented apps but rejected in the transaction-oriented apps.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 197-205"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000092/pdfft?md5=a7bb4247cfcf6752ca93c863b7a071ac&pid=1-s2.0-S2666764924000092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966302","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":"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":"7 3","pages":"Pages 189-196"},"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":"7 3","pages":"Pages 206-217"},"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":"7 3","pages":"Pages 181-188"},"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":"7 3","pages":"Pages 155-163"},"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":"7 3","pages":"Pages 164-180"},"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}