Pub Date : 2021-10-24DOI: 10.1080/17517575.2021.1992675
Wen-Lung Shiau, Xiaoqun Wang, Fei Zheng, Y. Tsang
ABSTRACT Cognition and emotion play important roles in information systems (IS) research, yet existing studies have not provided a comprehensive picture of these issues in the IS field. In this study, a citation network including 2,061 related academic articles published between 1996 and 2019 is established. Two novel indicators are proposed, through which 57 influential articles are identified, namely annual average degree centrality (AADC) and annual average betweenness centrality (AABC). A backward search process is performed preceding the co-citation analysis to exhaustively collect co-citation data. Finally, integrating multidimensional scaling analysis with clustering analysis, six core knowledge groups are revealed.
{"title":"Cognition and emotion in the information systems field: a review of twenty-four years of literature","authors":"Wen-Lung Shiau, Xiaoqun Wang, Fei Zheng, Y. Tsang","doi":"10.1080/17517575.2021.1992675","DOIUrl":"https://doi.org/10.1080/17517575.2021.1992675","url":null,"abstract":"ABSTRACT Cognition and emotion play important roles in information systems (IS) research, yet existing studies have not provided a comprehensive picture of these issues in the IS field. In this study, a citation network including 2,061 related academic articles published between 1996 and 2019 is established. Two novel indicators are proposed, through which 57 influential articles are identified, namely annual average degree centrality (AADC) and annual average betweenness centrality (AABC). A backward search process is performed preceding the co-citation analysis to exhaustively collect co-citation data. Finally, integrating multidimensional scaling analysis with clustering analysis, six core knowledge groups are revealed.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49589061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.1080/17517575.2021.1986861
Chi-Hua Chen, Genggeng Liu, Yu-Chih Wei, Zuoyong Li, Bon-Yeh Lin
ABSTRACT This research proposes a pre-signed response method based on online certificate status protocol (OCSP) request prediction. A request prediction method is proposed to analyse and predict potential volumes of certificate signing requests for a given time or period, so that responses to the requests can be generated and pre-signed during off-peak hours for load balancing. In our experiment, the OCSP request data in a certificate centre is collected and analysed for the evaluation of our proposed method. Our results show that the accuracy rate of our proposed method is about 96.17% in the prediction of traffic volumes.
{"title":"A pre-signed response method based on online certificate status protocol request prediction","authors":"Chi-Hua Chen, Genggeng Liu, Yu-Chih Wei, Zuoyong Li, Bon-Yeh Lin","doi":"10.1080/17517575.2021.1986861","DOIUrl":"https://doi.org/10.1080/17517575.2021.1986861","url":null,"abstract":"ABSTRACT This research proposes a pre-signed response method based on online certificate status protocol (OCSP) request prediction. A request prediction method is proposed to analyse and predict potential volumes of certificate signing requests for a given time or period, so that responses to the requests can be generated and pre-signed during off-peak hours for load balancing. In our experiment, the OCSP request data in a certificate centre is collected and analysed for the evaluation of our proposed method. Our results show that the accuracy rate of our proposed method is about 96.17% in the prediction of traffic volumes.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43627596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1080/17517575.2021.1989495
Hongfeng Yu, Akinola Ogbeyemi, W. Lin, Jingyi He, Wei Sun, W. Zhang
ABSTRACT This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as knowledge). The salient features of the model are: (1) separation of business functions from applications and enterprises, (2) three-layer architecture of the model (conceptual or semantic level, external or application level, internal or realisation level), and (3) integration of structured, semi-structured and non-structured data. To our best knowledge, the existing model or solution to EAI does not hold all the three features. A case study is presented to illustrate how the model works. The model can be used by an individual enterprise or a group of enterprises that form a network, e.g., a holistic supply chain network.
{"title":"A semantic model for enterprise application integration in the era of data explosion and globalisation","authors":"Hongfeng Yu, Akinola Ogbeyemi, W. Lin, Jingyi He, Wei Sun, W. Zhang","doi":"10.1080/17517575.2021.1989495","DOIUrl":"https://doi.org/10.1080/17517575.2021.1989495","url":null,"abstract":"ABSTRACT This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as knowledge). The salient features of the model are: (1) separation of business functions from applications and enterprises, (2) three-layer architecture of the model (conceptual or semantic level, external or application level, internal or realisation level), and (3) integration of structured, semi-structured and non-structured data. To our best knowledge, the existing model or solution to EAI does not hold all the three features. A case study is presented to illustrate how the model works. The model can be used by an individual enterprise or a group of enterprises that form a network, e.g., a holistic supply chain network.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45812622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-06DOI: 10.1080/17517575.2021.1909751
Mu-Yen Chen, E. Lughofer, E. Eğrioğlu
Nowadays, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, cloud and fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the cloud and fog environment, IoT applications are executed by the intermediate computing nodes, as well as the physical servers in cloud data centres. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of cloud and fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Apart from the Internet of Things (IoT) issue in cloud and fog computing, today, the data security and integrity problems are receiving attention gradually, which avoids malicious data stealing and amending in cloud and fog computing by hackers. Additionally, to fulfill the requirements of authentication, confidentiality, integrity, and non-repudiation, the development of cloud and fog Computing needs lightweight cryptography to reduce workloads and improve performance. With the advance of smart city and Artificial Intelligence (AI), cloud and fog computing plays the role of saving and calculating data; hence, the more experts and researchers in the fields of communications networks and information technology, the more ideas and thoughts to enhance the performance.
{"title":"Cloud & fog computing: intelligent applications","authors":"Mu-Yen Chen, E. Lughofer, E. Eğrioğlu","doi":"10.1080/17517575.2021.1909751","DOIUrl":"https://doi.org/10.1080/17517575.2021.1909751","url":null,"abstract":"Nowadays, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, cloud and fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the cloud and fog environment, IoT applications are executed by the intermediate computing nodes, as well as the physical servers in cloud data centres. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of cloud and fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Apart from the Internet of Things (IoT) issue in cloud and fog computing, today, the data security and integrity problems are receiving attention gradually, which avoids malicious data stealing and amending in cloud and fog computing by hackers. Additionally, to fulfill the requirements of authentication, confidentiality, integrity, and non-repudiation, the development of cloud and fog Computing needs lightweight cryptography to reduce workloads and improve performance. With the advance of smart city and Artificial Intelligence (AI), cloud and fog computing plays the role of saving and calculating data; hence, the more experts and researchers in the fields of communications networks and information technology, the more ideas and thoughts to enhance the performance.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"15 1","pages":"1197 - 1199"},"PeriodicalIF":4.4,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46749755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-14DOI: 10.1080/17517575.2021.1886331
Kai-Leung Yung, Lida Xu, Chris W. J. Zhang
Spacecraft informatics is one of the most exciting and contemporary research topics in recent years. Many countries are deploying related technologies such as AI, robotics, machine learning, etc., in the deep-space explorations. Moreover, considering the high-complexity, high cost and high risk involved in spacecraft, advanced technologies in information modelling, simulation, optimisation and decision support methods are required to improve the effectiveness, efficiencies, reliabilities and safety of the space operations (Du et al. 2017; Rui et al. 2014). The emerging informatics approach offers the benefit to the area of spacecraft regarding in-orbit spacecraft, satellites, space-stations of any types in deep-space exploration missions from ground control, user payload, space weather and conditions, remote sensing and telemetry, and many more spaceflight missions and activities of designing, forecasting, planning and control. To contribute the present and future space exploration and spacecraft development, in this special issue, we have collected excellent papers of research in spacecraft informatics. Each paper underwent a double-blind peer review by independent, anonymous expert referees. After the reviewing processes, eight highquality papers were accepted and are published in this issue. The first paper is ‘Optimisation problems and resolution methods in satellite scheduling and spacecraft operation: a survey’ by Xhafa and Ip (2019). This paper aims to study the state of the art in the satellite scheduling regarding the spacecraft design, operation and satellite deployment system. With heuristics methods, the constraint features in satellite mission planning, including window accessibility and visibility requirements can be addressed for producing smalland low-cost satellites. The second paper, entitled ‘Moon image segmentation with a new mixture histogram model’ by Hsu et al. (2019) is related to an application of image processing technology in the spacecraft. This paper aims to develop a histogram mixture model with genetic algorithm for improving the effectiveness in segmenting the moon surface image. Instead of the manual parameters measurement, the parameters can be obtained by a genetic algorithm. The results show that the proposed algorithm improved the drawbacks of previous non-parametric methods for moon image segmentation. In the papers, entitled ‘Blockchain adoption for information sharing: risk decisionmaking in spacecraft supply chain’ by Zheng et al. (2019) and ‘A framework for rocket and satellite launch information management systems based on blockchain technology’ by Li, Wang, and Zhang (2019), they employed blockchain technology for information management and sharing in the spacecraft supply chain. The use of blockchain technology allows the stakeholders in the spacecraft to (i) reduce transaction cost and risks, and (ii) improve the reliability and traceability of the spacecraft information to enhance the overall effecti
航天器信息学是近年来最热门、最具时代性的研究课题之一。许多国家都在深空探索中部署人工智能、机器人、机器学习等相关技术。此外，考虑到航天器的高复杂性、高成本和高风险，需要先进的信息建模、仿真、优化和决策支持方法技术来提高空间运行的有效性、效率、可靠性和安全性(Du et al. 2017;Rui et al. 2014)。新兴的信息学方法为航天器领域提供了好处，涉及在轨航天器、卫星、深空探测任务中的任何类型的空间站，包括地面控制、用户有效载荷、空间天气和条件、遥感和遥测，以及更多的空间飞行任务和设计、预测、规划和控制活动。为了对现在和未来的空间探索和航天器的发展做出贡献，我们在这期特刊中收集了航天器信息学研究的优秀论文。每篇论文都经过了独立的匿名专家评审的双盲同行评审。经过评审，8篇高质量论文被录用并发表于本期。第一篇论文是Xhafa和Ip(2019)的“卫星调度和航天器运行中的优化问题和解决方法:一项调查”。本文旨在从航天器设计、运行和卫星部署系统等方面研究卫星调度技术的发展现状。利用启发式方法，可以解决卫星任务规划中的约束特征，包括窗口可达性和可见性要求，以生产小成本卫星。Hsu et al.(2019)的第二篇论文《基于新型混合直方图模型的月球图像分割》涉及图像处理技术在航天器上的应用。为了提高月球表面图像分割的有效性，提出了一种基于遗传算法的直方图混合模型。通过遗传算法获得参数，代替人工测量参数。结果表明，该算法改善了以往非参数分割方法的不足。在Zheng等人(2019)的论文《区块链用于信息共享:航天器供应链中的风险决策》和Li、Wang和Zhang的论文《基于区块链技术的火箭和卫星发射信息管理系统框架》(2019)中，他们采用区块链技术进行航天器供应链的信息管理和共享。区块链技术的使用使航天器的利益相关者能够(i)降低交易成本和风险，(ii)提高航天器信息的可靠性和可追溯性，以提高供应链的整体有效性和效率。Tang等人(2019)的论文“使用信念规则库对航天器关键部件的健康状况进行估计”开发了一种半定量方法来检查企业信息系统2021,VOL. 15, NO. 5。8, 1019-1021 https://doi.org/10.1080/17517575.2021.1886331
{"title":"Spacecraft Informatics","authors":"Kai-Leung Yung, Lida Xu, Chris W. J. Zhang","doi":"10.1080/17517575.2021.1886331","DOIUrl":"https://doi.org/10.1080/17517575.2021.1886331","url":null,"abstract":"Spacecraft informatics is one of the most exciting and contemporary research topics in recent years. Many countries are deploying related technologies such as AI, robotics, machine learning, etc., in the deep-space explorations. Moreover, considering the high-complexity, high cost and high risk involved in spacecraft, advanced technologies in information modelling, simulation, optimisation and decision support methods are required to improve the effectiveness, efficiencies, reliabilities and safety of the space operations (Du et al. 2017; Rui et al. 2014). The emerging informatics approach offers the benefit to the area of spacecraft regarding in-orbit spacecraft, satellites, space-stations of any types in deep-space exploration missions from ground control, user payload, space weather and conditions, remote sensing and telemetry, and many more spaceflight missions and activities of designing, forecasting, planning and control. To contribute the present and future space exploration and spacecraft development, in this special issue, we have collected excellent papers of research in spacecraft informatics. Each paper underwent a double-blind peer review by independent, anonymous expert referees. After the reviewing processes, eight highquality papers were accepted and are published in this issue. The first paper is ‘Optimisation problems and resolution methods in satellite scheduling and spacecraft operation: a survey’ by Xhafa and Ip (2019). This paper aims to study the state of the art in the satellite scheduling regarding the spacecraft design, operation and satellite deployment system. With heuristics methods, the constraint features in satellite mission planning, including window accessibility and visibility requirements can be addressed for producing smalland low-cost satellites. The second paper, entitled ‘Moon image segmentation with a new mixture histogram model’ by Hsu et al. (2019) is related to an application of image processing technology in the spacecraft. This paper aims to develop a histogram mixture model with genetic algorithm for improving the effectiveness in segmenting the moon surface image. Instead of the manual parameters measurement, the parameters can be obtained by a genetic algorithm. The results show that the proposed algorithm improved the drawbacks of previous non-parametric methods for moon image segmentation. In the papers, entitled ‘Blockchain adoption for information sharing: risk decisionmaking in spacecraft supply chain’ by Zheng et al. (2019) and ‘A framework for rocket and satellite launch information management systems based on blockchain technology’ by Li, Wang, and Zhang (2019), they employed blockchain technology for information management and sharing in the spacecraft supply chain. The use of blockchain technology allows the stakeholders in the spacecraft to (i) reduce transaction cost and risks, and (ii) improve the reliability and traceability of the spacecraft information to enhance the overall effecti","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"15 1","pages":"1019 - 1021"},"PeriodicalIF":4.4,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41352518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-11DOI: 10.1080/17517575.2021.1907863
Yuta Kitano, Tetsuo Yamada, K. Tan
ABSTRACT Manufacturing companies publish a variety of own data via social media platforms Twitter and also technical reports concerning the new technologies developed in-house. This study aims to analyse corporate social networking service data and technical reports to bring to light the technological innovations taking place in Japanese manufacturing companies. It uses text mining, a text data analysis method to extract useful information (from published technical reports) by dividing normal text data into words and phrases. The results show the strategic differences that exist between Twitter and technical reports in terms of bringing new information to consumers and companies.
{"title":"Technological innovation, new solutions, branding, and promotion: Twitter and technical report use in Japanese’s companies","authors":"Yuta Kitano, Tetsuo Yamada, K. Tan","doi":"10.1080/17517575.2021.1907863","DOIUrl":"https://doi.org/10.1080/17517575.2021.1907863","url":null,"abstract":"ABSTRACT Manufacturing companies publish a variety of own data via social media platforms Twitter and also technical reports concerning the new technologies developed in-house. This study aims to analyse corporate social networking service data and technical reports to bring to light the technological innovations taking place in Japanese manufacturing companies. It uses text mining, a text data analysis method to extract useful information (from published technical reports) by dividing normal text data into words and phrases. The results show the strategic differences that exist between Twitter and technical reports in terms of bringing new information to consumers and companies.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"15 1","pages":"1683 - 1712"},"PeriodicalIF":4.4,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-09DOI: 10.1080/17517575.2020.1830180
Gunasekaran Manogaran, H. Qudrat-Ullah, Bharat S. Rawal Kshatriya
This special issue aims to bring out recent advances in cyber-physical systems (CPS) and its applications. CPS is a new emerging paradigm with widespread applications such as intelligent manufacturing, smart grid, smart manufacturing, etc. Usually, CPS applications are complex, and it is often difficult to build and manage in a real-time environment. Currently, energy management remains to be a critical issue, especially with automobile industries. To effectively deal with energy optimisation problems across smart scheduling systems, a Multiple Fuzzy Aggravated Energy Scheduling Approach (MFAESA) is proposed to incorporate fuzzy algorithms to deal with energy loss problems. This algorithm searches for the network idle time and optimises the energy usage of IoT efficiently assisted automobile industries. This increases performance measures and reduces system execution time. Further, this approach is highly accurate and protects energy loss across the IoT network in a more optimised way. Supply chain management is one of the most prominent applications of CPS. This special issue also focuses on exploring the most accurate and fault-tolerant solutions for CPS assisted supply chain management systems. Devices, including target hardware, software, and operating environment, are more susceptible to vulnerable operations when functioning across the IoT systems. A linear approximation based fuzzy model is used to identify defective components in the supply chain management systems. The use of roughest approximation techniques eliminates the defects in the identification of faulty components and eliminates ambiguity measures. In addition, this approach helps to measure faults across the dynamic modules of the system. Currently, information management across physical networks remains to be a significant issue. Especially with cybersecurity assisted IoT systems. This special issue presents a deep reinforcement learning-based solution to deal with enterprise information management and its integration with intelligent physical systems. It efficiently
{"title":"Intelligent autonomous cyber-physical systems and applications","authors":"Gunasekaran Manogaran, H. Qudrat-Ullah, Bharat S. Rawal Kshatriya","doi":"10.1080/17517575.2020.1830180","DOIUrl":"https://doi.org/10.1080/17517575.2020.1830180","url":null,"abstract":"This special issue aims to bring out recent advances in cyber-physical systems (CPS) and its applications. CPS is a new emerging paradigm with widespread applications such as intelligent manufacturing, smart grid, smart manufacturing, etc. Usually, CPS applications are complex, and it is often difficult to build and manage in a real-time environment. Currently, energy management remains to be a critical issue, especially with automobile industries. To effectively deal with energy optimisation problems across smart scheduling systems, a Multiple Fuzzy Aggravated Energy Scheduling Approach (MFAESA) is proposed to incorporate fuzzy algorithms to deal with energy loss problems. This algorithm searches for the network idle time and optimises the energy usage of IoT efficiently assisted automobile industries. This increases performance measures and reduces system execution time. Further, this approach is highly accurate and protects energy loss across the IoT network in a more optimised way. Supply chain management is one of the most prominent applications of CPS. This special issue also focuses on exploring the most accurate and fault-tolerant solutions for CPS assisted supply chain management systems. Devices, including target hardware, software, and operating environment, are more susceptible to vulnerable operations when functioning across the IoT systems. A linear approximation based fuzzy model is used to identify defective components in the supply chain management systems. The use of roughest approximation techniques eliminates the defects in the identification of faulty components and eliminates ambiguity measures. In addition, this approach helps to measure faults across the dynamic modules of the system. Currently, information management across physical networks remains to be a significant issue. Especially with cybersecurity assisted IoT systems. This special issue presents a deep reinforcement learning-based solution to deal with enterprise information management and its integration with intelligent physical systems. It efficiently","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"15 1","pages":"909 - 910"},"PeriodicalIF":4.4,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17517575.2020.1830180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49483641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-02DOI: 10.1080/17517575.2021.1959651
Huosong Xia, Wuyue An, Genwang Liu, Runjiu Hu, Z. Zhang, Yuan Wang
ABSTRACT Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities – picture, review, and scoring .We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users.
{"title":"Smart recommendation for tourist hotels based on multidimensional information: a deep neural network model","authors":"Huosong Xia, Wuyue An, Genwang Liu, Runjiu Hu, Z. Zhang, Yuan Wang","doi":"10.1080/17517575.2021.1959651","DOIUrl":"https://doi.org/10.1080/17517575.2021.1959651","url":null,"abstract":"ABSTRACT Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities – picture, review, and scoring .We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17517575.2021.1959651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42022730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-02DOI: 10.1080/17517575.2021.1959652
N. Janjua, Falak Nawaz, D. Prior
ABSTRACT In this study, we develop a novel methodology to identify supply chain disruption events using Twitter feeds in real time. Underpinned by advances in Natural Language Processing (NLP) and machine learning, we propose an approach that includes a state-of-the-art variant of Conditional Random Field (CRF) model for event annotation, location-based clustering of the annotated events, and a fuzzy inference system to evaluate supply chain risk. We validate the new approach through a text corpus derived from a Twitter data stream, which is a popular method in NLP. The results show that the proposed model outperforms the baseline model.
{"title":"A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter","authors":"N. Janjua, Falak Nawaz, D. Prior","doi":"10.1080/17517575.2021.1959652","DOIUrl":"https://doi.org/10.1080/17517575.2021.1959652","url":null,"abstract":"ABSTRACT In this study, we develop a novel methodology to identify supply chain disruption events using Twitter feeds in real time. Underpinned by advances in Natural Language Processing (NLP) and machine learning, we propose an approach that includes a state-of-the-art variant of Conditional Random Field (CRF) model for event annotation, location-based clustering of the annotated events, and a fuzzy inference system to evaluate supply chain risk. We validate the new approach through a text corpus derived from a Twitter data stream, which is a popular method in NLP. The results show that the proposed model outperforms the baseline model.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17517575.2021.1959652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49288807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-14DOI: 10.1080/17517575.2021.1941275
Milan Zdravković, H. Panetto, G. Weichhart
ABSTRACT This paper considers Enterprise Information Systems functional architecture and carries out review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. Enhanced capabilities are identified and proposed as AI services. AI-enablement implements improved decision-making or automation by using Machine Learning models or logic-based systems. It is a process of the enterprise transformation leading to the convergence of the four major disruptive technologies, namely Industrial Internet of Things, Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence.
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