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International Journal of Information Technology and Management

International Journal of Information Technology and Management (IJITM)

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International Journal of Information Technology and Management (45 papers in press)

Regular Issues

  • Impact of E-commerce on supply chain management   Order a copy of this article
    by Saleeshya P.G, Rahul R 
    Abstract: E-commerce is web-enabled technology that brought significant changes in the supply chain activities of anindustry. Supply chain management has become a major strategy in manufacturing and service industry. An attempt has made in this paper on identifying the significant factors that affect the e-commerce adoption in the supply chain of a company, identifying the effects of e-commerce on different aspects of supply chain. The study also identifies the major performance parameters of a supply chain and effect of e-commerce adoption on these supply chain variables. An attempt has been made to propose a conceptual model for the study based on the existing literature and field study; the model is validated by academic experts and also experts in industry. Correlation analysis is being carried out to find gap between the ideal and actual situation.rnrn
    Keywords: supply chain; E-commerce; correlation; conceptual model.

  • Determinants of Success and Failure of Knowledge Transfer in Information Systems Offshoring: A Ranking-Type Delphi Study   Order a copy of this article
    by Artur Strasser, Susanne Strahringer, Markus Westner 
    Abstract: The transfer of knowledge from client to service provider poses major challenges in information systems (IS) offshoring projects. Knowledge transfer directly affects IS offshoring success. Therefore, associated challenges must be overcome. Our study examines the determinants of success and failure of knowledge transfer in IS offshoring projects based on a ranking-type Delphi study. We questioned 32 experts from Germany, each with more than ten years of experience in near- or offshore initiatives to seek a consensus among them. We identified 19 success and 20 failure determinants. These determinants are ranked in order of importance using best-worst scaling. Aspects of closer cooperation are critical for effective knowledge transfer. This includes regular collaboration, willingness to help and support, and mutual trust. In contrast, critical determinants of failure are concerned with fears and fluctuation of human resources. Hidden ambiguities or knowledge gaps, an unwillingness and disability to share knowledge, and high fluctuation of human resources negatively impact knowledge transfer.
    Keywords: best-worst scaling; BWS; delphi; determinants of success; determinants of failure; information systems; IS; information systems offshoring; knowledge transfer; ranking-type delphi.

  • How to evaluate brand extension in the mobile Internet environment   Order a copy of this article
    by Wenlong Zhu, Shiye Wang 
    Abstract: How to successfully implement a brand extension has been a common topic among global enterprises. Mobile Internet (M-Internet), a new information and communication technology, creates suitable conditions for enterprise brand extension. However, most researches related to brand extension evaluation focus on traditional information technologies presently. Little research has addressed M-Internet. From the perspective of consumer, this study constructs brand extension evaluation model based on the Task-Technology Fit (TTF) and Aaker and Keller Model (A & K Model), and analyzes the influencing mechanism of brand extension evaluation by using the Structural Equation Modeling (SEM). The final results show that technical characteristics of M-Internet produce a positive effect on attitude of parent brand. Furthermore, attitude of parent brand influences the brand extension evaluation, brand trust and perceived fit positively. Lastly, brand extension evaluation is subject to positive impact of brand trust and perceived fit besides attitude of parent brand. The theoretical and practical implications of this study are discussed lastly.
    Keywords: Mobile Internet; Brand extension evaluation; Structural Equation Modeling; Mediation effect.

  • QoE-based service differentiation: An analysis of the business implications for the mobile services market   Order a copy of this article
    by Luis Guillermo Martinez Ballesteros, Per Jonny Nesse, Jan Markendahl 
    Abstract: Mobile network operators (MNOs) face a future characterized with new challenges, such as growing data consumption, a slowdown in subscriber growth and reduced revenues due to the success of over-the-top providers. To remain competitive, MNOs must offer affordable services and provide innovative strategies to retain current customers. Quality of Experience (QoE) is a well-established methodology for measuring and understanding the overall level of customer satisfaction and has also been presented as a way to improve telecommunication services. Even though QoE can be used to solve problems, such as customer loyalty and optimization of network resources in mobile networks, there is still a lack of knowledge on how the MNOs can take advantage of QoE and its potential benefits. In this paper, we explored the implications of the incorporation of QoE feedback in mobile networks at the business level. The analysis, which is based on a combination of value network configuration and business model analysis of scenarios, shows that value-added offers of differentiated and personalized services can be seen as alternatives to generate new revenue streams in the mobile service market. An important finding from our study is that, due to the nature of the challenges facing the mobile services industry, a QoE analysis cannot be limited to only a technical discussion but needs to be combined with an informed analysis of the business implications of the proposed solution.
    Keywords: Quality of Experience; Business Models; Mobile Networks; Service Differentiation.

  • Research on Evaluation of Network Payment Security   Order a copy of this article
    by Xiang Xie, Ying Cui 
    Abstract: The paper collects the data about the network payment fraud through the questionnaire and then analyzes the data. We propose a prediction model based on the hybrid support degree Apriori algorithm, and find out the factors that are closely related to the success of cheating. Then we find out the nodes of the decision and their importance tree use the ID3 algorithm to construct the decision tree, construct and verify the evaluation index system of the network payment from the user's point of view, considering the results of association rules and decision tree, combined with principal component analysis and text mining results, and then determine the weight using entropy method. So we can determine the probability of being deceived according to the situation of different people, not only remind people to be vigilant, but also provide reference for the community.
    Keywords: Apriori algorithm; ID3 algorithm; Internet payment security; styling; Evaluation System; Principal component analysis; entropy method.

  • Scrutinizing medical practitioners twitter feeds analysis   Order a copy of this article
    by Arushi Jain, Vishal Bhatnagar, Nilanjan Dey, Amira Ashour, Fuqian Shi 
    Abstract: Mining of social media data has found widespread applications in recent times. Twitter feeds and facebook posts are being used to device product marketing strategies, sentiment analysis, financial predictions and forebode alarming situations. Twitter feeds analysis can be applied for analyzing the behavior and experiences of medical practitioners. Doctors informal conversations on Twitter can provide deep insights about their work experiences, their concerns about the profession, their feelings- pathos or excitement they feel and the affecting conditions. In the present work, twitter feed of Doctors along with the Twitter hashtags are used to collect data from tweets with hashtags such as #DoctorProblems. Afterward, data analysis was performed to determine the major problems faced by the members of the medical fraternity. These problems were categorized into five main categories. Further, based on these categories, the multi-label Na
    Keywords: Big data; Hadoop distributed file system; MapReduce; Naïve Bayes multilabel classifier; Tweets; Evaluation based measure; Label based measure.

  • Big Data Block Impact within Big Data Environment   Order a copy of this article
    by Ron Ziv, Oded Koren, Nir Perel 
    Abstract: Handling data is becoming more and more complex. A higher velocity of data is created as more people have access to data generating devices such as computers, mobile phones, medical devices, home appliances, etc. Data files, such as user activity logs, system logs and so on, are stored in HDFS big data platform in various sizes, considering the business requirements, infrastructure parameters, administration decisions, etc. Dividing the data files (in various volumes), without taking into consideration the HDFS predefined block size may create performance issues which can affect the systems activity. This paper presents how HDFS block design affects the performance of Apache Hadoop
    Keywords: architecture; HDFS; performance; big data.

  • A reliability and security enhanced framework for cloud-based storage systems   Order a copy of this article
    by Peng Xiao 
    Abstract: In cloud computing environments, reliable and secure data storage service plays a more and more important role in many data-intensive applications. However, existing storage systems either fail to provide them or provide them in a cost-ineffective manner. To provide better storage service in nowadays cloud environments, we propose a novel framework called Reliability and Security enhanced Cloud Storage (RSCS), which consists of several well-designed components to improve low-level data reliability as well as guarantee up-level data accessing security. In the RSCS framework, a simple yet effective file system scheme is proposed, which can mirror striped data among different nodes to improve the reliability while maintaining a high aggregated throughput. A centralized lease-based mechanism is designed to allow simultaneous accesses to different portions of a single file while maintaining the multiple-reader-single-writer semantics to each requested data portion. Finally, we provide a secure data accessing tunnel technology which allows the RSCS to establish secure communication channels between users and storage nodes without introducing too many extra costs. Extensive experiments are conducted in a real-world cloud storage platform, and the results indicate that the performance of the proposed RSCS framework can easily meet the requirements for most of current cloud systems.
    Keywords: cloud storage; data security; replication service; file system.

  • Openstack: a virtualization overview   Order a copy of this article
    by Pedro Martins, Faouzi Mechraoui, Filipe Cladeira 
    Abstract: The major cloud computing software companies offer a new concept, on which resources are virtualized to provide the service on the internet. Currently, there are multiple service providers, and additional options to virtualize services on premises. OpenStack is an open source alternative to create virtual local or cloud setups, which supports the petabytes of data, unlimited scale, and configurable networking. These features make this tool suitable for large scenarios virtualization, reducing maintenance costs and optimizing hardware resources utilization (e.g., schools, government). This paper presents an overview of the study of OpenStack software, oriented to build a scalable hosting architecture suitable for an educational setup. Functional and architectural details are discussed to implement unique cloud computing to fit the virtualization purposes. An experimental virtualization setup is described in the scope of an educational scenario. Finally, a guideline to configure OpenStack is given.
    Keywords: OpenStack; IaaS; Virtualization; Cloud computing; Open Source.

  • Consequential effects of leading technology-driven offensive strategy in a universal bank   Order a copy of this article
    by Asare Yaw Obeng, Alfred Coleman 
    Abstract: Globally, banking industry is saddled with intense competition. Management of banking firms are required to formulate effective strategies to drive their capabilities to compete, discover and defend their positions in a competitive industry. ?n Ghana, universal banks are leveraging the capabilities of information technology to devise technology-driven offensive strategies. The core of these strategies is embarking on innovative activities to enhance performance and respond to business challenges. A qualitative analysis was performed using primary data from 17 participants. Using NVivo, consequential effects of IS-technological innovation (ISTI) on business challenges, innovation performance, operational performance; and specific moderating factors of ISTI were assessed. Strategic-IS Project impacts strong (r=0.634601) and positive on ISTI than the other moderators. ISTI impacts strongly (r=0.644951) and positively on operational performance. With r=0.7422, innovation performance positively and strongly influences operational performance. ISTI impacts positively on business challenges.
    Keywords: Bank; Ghana; industry threats; information systems; linear regression; moderating factors; offensive strategy; technological innovation.

  • AQINM: an adaptive QoS management framework based on intelligent negotiation and monitoring in cloud   Order a copy of this article
    by Zeng Saifeng 
    Abstract: Recently, more and more federated cloud platforms have been constructed to deal with non-trivial large-scale applications, which typically require certain level of quality-of-service (QoS) guarantee. However, most of existing cloud-oriented QoS solutions are likely to introduce extra overheads on either resource allocation or task execution, which is especially true in federated cloud environments. In this paper, we present a lightweight QoS-enhancing middleware, namely Adaptive QoS Management based on Intelligent Negotiation and Monitoring (AQINM), which provides three QoS-enhancing services including policy management, service-layer-agreement (SLA) negotiation, and SLA monitoring. Unlike the conventional QoS-enable middleware, these services in the AQINM framework introduce several novel mechanisms to offer more cost-effective and efficient solutions to enforcing the QoS management in federated clouds. The implementation of our AQINM framework are tested in a campus federated cloud platform by using different applications as experimental benchmarks, and its performance are compared with other similar solutions. The experimental results indicate that the proposed AQINM can significantly reduce the costs of SLA negotiation and monitoring for large-scale cloud application that deployed in federated cloud environments.
    Keywords: cloud computing; quality of service; service layer agreement; resource virtualization.

  • Global Information Technology Outsourcing: Issues of Attractiveness of Some Regions in Eastern Europe   Order a copy of this article
    by Grigori Feigin, Anna Hayduk 
    Abstract: The article deals with the problem of the development of global information technology outsourcing. Based on the summary of main theoretical approaches used to explain the phenomenon of outsourcing, the conditions for making decisions on information technology outsourcing (ITO) nationally and globally are identified. The authors overview of the chances (positive effects for companies) arising from the ITO application specific to the national and global outsourcing is provided. The causes of the ITO insufficient spread at the present stage are indicated. A model classifying the factors that influence the attractiveness of the national economies of some countries for ITO is proposed. The attractiveness of the countries of the Eastern European region for ITO is assessed based on the example of a comparative analysis of Russia and Ukraine.
    Keywords: information technology outsourcing; supplier company; client company; chances and risks; factors of attractiveness of regions; Eastern Europe; comparative analysis; Russia; Ukraine.
    DOI: 10.1504/IJITM.2021.10049674
  • Applying Machine Learning Algorithms To Determine And Predict The Reasons And Models For Employee Turnover   Order a copy of this article
    by Shardul Shankar, Ranjana Vyas, Vijayshri Tewari 
    Abstract: In recent years, organizations have struggled with the turnover of employees, which has become one of the biggest issues that not only have inadvertent consequences to an organizations growth, productivity, and performance; but also has negative implications on the intrinsic cost associated with it. To cater to this problem, one such method is the use of Machine Learning Algorithms. But one of the biggest issues in HR Information System (HRIS) analysis is the presence of noise in data, leading to inaccurate predictions. This paper tries to examine the efficiency of six such Algorithms, to determine the robustness, accuracy in real-time analysis of data, and then use that companys historical data to predict employee turnover for the present year. The dataset was mined from the HRIS database of a global organization in the US and Canada in the span of 10 years to compare these algorithms to examine voluntary turnover, using Python and RStudio analytical tools.
    Keywords: Employee Turnover; Machine Learning; Predictive Algorithms; Classification; Voluntary Turnover.

  • Industry 4.0 projects: a set of new individual competencies   Order a copy of this article
    by Tainá Alves Townsend, Cristiane Drebes Pedron, Rodrigo Menezes De Souza, Roberto Lima Ruas 
    Abstract: The current paper aims to identify the individual competencies of collaborators in industry 4.0 projects. We used the Delphi method with three rounds as a research methodology. Sixty-three experts engaged with industry 4.0 projects, professionals and academics, participated in the study. A set of individual competencies related to industry 4.0 was identified: (1) technical (technical skills, systemic process comprehension, IT security skills, project management tendency, analytical skills, and business knowledge), (2) personal (flexibility, learning motivation, decision-making, ambiguity tolerance, and ability to work under pressure), (3) methodological (creativity, digital mindset, and entrepreneur thinking) and (4) social aspects (communication skills, team-working skills, being committed and proactive, conflict resolution, and leadership skills). The findings led to a list of competencies for project managers and other team members in the industry 4.0' context. This result provides new insights related to personnel development in project-managed environments and educational guidance for the future workforce.
    Keywords: Industry 4.0; Competencies; Project management; Workforce; Skills; Individual Competencies; Digital Transformation; Delphi Method; Competences; Individual Competences.

  • If You Cannot Fly, Then Run: A Model of BIM Implementation Taxonomies and Thresholds   Order a copy of this article
    by Oluseye Olugboyega 
    Abstract: The barriers to BIM adoption are numerous and overwhelming. These barriers must be progressively overcome through a recursive BIM implementation strategy and assessment. The aim of this paper is to identify the key reduction indicators for tracking BIM adoption barriers and establish whether the key reduction indicators will provide a model of BIM implementation taxonomies and thresholds for assessing BIM implementation performance. Meta-Analysis methodology was used to synthesize the diverse findings. These key reduction indicators were categorized into three BIM implementation thresholds: BIM advanced industry, BIM emerging industry, and BIM frontier industry. It was found that BIM implementation taxonomies have different levels of the implementation plan, levels of market effectiveness, and levels of objectives The study concluded that the proposed model will help streamline the needs and advise on the BIM implementation needs of the various construction industry, most especially the developing construction industries.
    Keywords: BIM; BIM implementation; BIM adoption; BIM adaptation; BIM application; BIM utilization; BIM implementation taxonomies; BIM implementation thresholds.

  • Dynamic options hedging model under mark-to-market risk   Order a copy of this article
    by Dongwei Shi, Yanyin Li, Xing Yu 
    Abstract: In this article, a model for options hedging under the budget and margin calls restrictions for buying put options and selling call options, respectively, is proposed. The proposed models are then solved by using the innovative interior point approach. This study analyzes the effectiveness of Chinese SSE 50ETF options for hedging with and without the addition of margin calls. We find that the hedging approach of net buying put options or net selling call options is less profitable than the hedging strategy of buying call options while simultaneously selling put options. To assess the validity of the study's conclusions, we examine the hedging efficiencies of several approaches when the underlying price has an upward tendency and find that the options hedging suggested in this research is still the best alternative. We advise investors to use call options and put options with lower strike prices for hedging in the sensitivity analysis.
    Keywords: Options hedging; Mark-to-market risk; Margin calls; Dual interior point algorithm.

  • Multilevel secure storage method of electronic documents based on Hash function   Order a copy of this article
    by Runhua Miao 
    Abstract: In order to improve the storage security and efficiency of electronic documents, a new multi-level secure storage method of electronic documents based on Hash function is proposed in this paper. Firstly, the vector space model is constructed, and the electronic document data is divided into fixed size data blocks by using the idea of linear segmentation. Then, after normalisation, Hash function is used to encrypt each data block. Finally, according to the encryption results, the compressed sensing method is used for multi-level secure storage of electronic documents. The experimental results show that, compared with the traditional storage methods, this method has strong encryption performance, and the maximum intrusion rate is no more than 0.5%, which can ensure the security of electronic documents. This method can improve the storage efficiency of electronic documents.
    Keywords: Hash function; electronic document; multi-level security storage; data segmentation.
    DOI: 10.1504/IJITM.2022.10051753
  • An Enterprise financial credit risk measurement method based on differential evolution algorithm   Order a copy of this article
    by Lixia Du, Xin An 
    Abstract: In order to reduce the time cost and risk misjudgement rate of financial information risk measurement, this paper proposes a new enterprise financial credit risk measurement method based on differential evolution algorithm. Firstly, after preprocessing the enterprise financial credit risk data and determining the location of the clustering centre, a differential evolution automatic clustering model is constructed. Secondly, according to the clustering results, the differential evolution algorithm is used to measure the basic process of enterprise financial credit risk. Finally, the improved differential evolution algorithm is used for iterative measurement to achieve enterprise financial credit risk data measurement. The experimental results show that the time cost of the proposed method for enterprise financial credit risk measurement can be controlled within 0.4 s, and the error rate is not more than 1% under the condition of 1,000 data.
    Keywords: differential evolution algorithm; corporate finance; credit risks; measurement method.
    DOI: 10.1504/IJITM.2022.10051754
  • Performance evaluation method of human resource management based on Chaotic Algorithm.   Order a copy of this article
    by Lina Si, Zhanlei Shang 
    Abstract: This paper proposes a new method of HR management performance evaluation based on chaotic algorithm. First of all, to improve the evaluation performance as the research goal, design the guiding principles of evaluation. Second, under the guidance of the principle, the multi type data of HR management are collected, and the chaotic algorithm is used to extract the characteristics of the data. Finally, after constructing the index system, calculate the weight and construct the evaluation function to complete the overall management performance evaluation. The test results show that the evaluation performance of the method has been improved. On the basis of shortening the evaluation time, the evaluation accuracy has been improved to 97.68%.
    Keywords: chaotic algorithm; human resources; management performance evaluation; index system.
    DOI: 10.1504/IJITM.2022.10051755
  • A data integrity detection method for accounting informatization based on homomorphic hash function   Order a copy of this article
    by Zhao Guang, Zhi Li 
    Abstract: In order to solve the problems of low data detection accuracy and high detection time overhead, this paper proposes an accounting information data integrity detection method based on homomorphic hash function. First, the accounting data is collected by data mining method and the strong relevance of the data is determined by association rules. Then, set the distance matrix to determine the data key points, match the niche factor between the data key points, and complete the feature extraction. Finally, the binary code is used to mark the accounting information data, and the anti-collision of homomorphic hash function is used to complete the projection of accounting data, so as to realise the data integrity detection. The results show that the detection accuracy of this method is up to 98%, and the detection time overhead is within 4S, which shows that this method can effectively improve the integrity detection effect.
    Keywords: homomorphic hash function; accounting informatisation; data detection; integrity: association rules.
    DOI: 10.1504/IJITM.2022.10051756
  • New business management model of enterprises based on data-driven   Order a copy of this article
    by Xiaofeng Zhang 
    Abstract: In the current enterprise management mode, there are problems such as low efficiency of enterprise management data processing and reducing the economic benefits of enterprises, which affect the rapid development of enterprises. In order to solve this problem, this paper studies the new business management model of enterprises based on data-driven. Build a data-driven enterprise management mode framework, integrate enterprise management data with KNN algorithm, and calculate user access trust and reliability values with trust management model to improve data processing efficiency and data security. Based on the digital processing of enterprise management data, the development strategy of new business management mode is given. The experimental results show that after applying the management mode designed in this paper, the maximum profit of the enterprise can reach 20.5 million yuan, and the maximum value of the enterprise data processing time is only 6.03 s, which proves that the designed management mode is more efficient for the enterprise management data processing, and can effectively improve the enterprise economic income, and has certain practical application value.
    Keywords: data-driven; business management model; data integration; KNN algorithm; trust management model; development strategy.
    DOI: 10.1504/IJITM.2022.10051929
  • A Recognition method of abnormal learning behavior in MOOC online education based on background subtraction   Order a copy of this article
    by Hongmei Wan 
    Abstract: In order to overcome the problems of high time-consuming and poor recognition accuracy of learning behaviour recognition, this paper proposes an abnormal learning behaviour recognition method for MOOC online education based on background subtraction. Firstly, the characteristics of students’ abnormal learning behaviours are collected and extracted. Then, the background difference algorithm is used to obtain the foreground object and background of the learning image, and the image pixels are classified. Finally, the mean background method is used to obtain the learning background, the abnormal behaviour recognition classifier is designed, and the background subtraction method is used to realise the abnormal learning behaviour recognition. The results show that the recognition accuracy of this method is as high as 98.32%, the recognition time is only 0.52 s, and the recognition recall rate is as high as 96.7%, indicating that this method can improve the recognition effect of abnormal learning behaviour.
    Keywords: background subtraction; binarisation treatment; mean background method; background difference method; online education.
    DOI: 10.1504/IJITM.2022.10051930
  • An evaluation method of government digital service quality based on big data   Order a copy of this article
    by Siguang Dai, Zhongming Tang, Ling Zhou 
    Abstract: Aiming at the problems of low accuracy and poor reliability of government service quality evaluation, this paper proposes a government digital service quality evaluation method based on big data. Firstly, the tree structure of index selection was obtained by big data analysis method, and the service quality evaluation index was selected. Secondly, the factor analysis method is used to analyse the reasonability of each evaluation factor and construct the evaluation system. Secondly, it obtains the government digital service data, and finally calculates the weight of service quality evaluation index. According to the clustering of big data, the fuzzy language is integrated and processed to realise the evaluation of government digital service quality. The results show that the evaluation time of the proposed method is not more than 29 s, and the consistency coefficient value of quality evaluation accuracy is 0.79. It has high evaluation efficiency and accuracy.
    Keywords: big data; government digital service; quality evaluation; evaluation index; fuzzy logic.
    DOI: 10.1504/IJITM.2022.10051931
  • A Performance evaluation method of new business model based on grey correlation algorithm   Order a copy of this article
    by Yan Wang 
    Abstract: A new business model performance evaluation method based on grey correlation algorithm is designed to solve the problems of large evaluation error and low key of screening indicators in the new business model performance evaluation. First, analyse the new business model and screen the performance evaluation indicators of the new business model; then, the clustering algorithm is used to determine the cluster family of each index, extract the performance evaluation index characteristics of the new business model, and construct the performance evaluation index system. Finally, the grey correlation algorithm is used to determine the grey correlation degree between the indicators, quantify the evaluation indicators, build a grey correlation model for the performance evaluation of the new business model, and realise the performance evaluation. The experimental results show that the evaluation error of the proposed evaluation method is only 2%, and the key degree of the selected index is higher than 90%, which is increased by more than 5%. This method has higher practical application value.
    Keywords: grey correlation algorithm; new business model; performance evaluation; clustering algorithm; main sequence; correlation sequence; consistency check.
    DOI: 10.1504/IJITM.2022.10052229
  • Research on digital English teaching materials recommendation based on improved machine learning   Order a copy of this article
    by Miao Ma 
    Abstract: In order to overcome the problems of low accuracy, time-consuming and low user satisfaction in traditional methods, a digital English teaching materials recommendation method based on improved machine learning is proposed. Firstly, use web crawlers to obtain the data of digital English teaching platform, and use Word2vec model data for training to obtain the data feature vector. Secondly, K-means algorithm is used to cluster users according to feature vectors, and multi-Markov chains are used to predict user interest. Finally, the decision tree algorithm in machine learning is improved on the gradient boosting framework, and the digital English teaching materials are recommended by using the improved algorithm and the user interest prediction results. The experimental results show that the accuracy of this method is more than 96%, the average time of digital English teaching materials recommendation is 76.1 ms, and the average user satisfaction is 96.6.
    Keywords: improved machine learning; digitisation; English teaching; data recommendation; multiple Markov chains; decision tree.
    DOI: 10.1504/IJITM.2022.10052230
  • The personalized classification of brand promotion information based on K-means algorithm   Order a copy of this article
    by Xi Li  
    Abstract: In order to improve the efficiency of personalised classification of brand promotion information and shorten the time of personalised classification, this paper proposes a personalised classification method of brand promotion information based on K-means algorithm. First, collect brand promotion information, and calculate the text relevance of brand promotion information through weighting factors; Secondly, the attribute division of extension information is carried out by using the three branch decision-making theory; then, the information features of brand promotion are extracted by capsule network; Finally, calculate the similarity between different brand promotion information, obtain the brand promotion information classification function, and realise the personalised classification of brand promotion information through k-means algorithm. The experimental results show that the classification accuracy of this method is 98.08%, and the time of personalised information classification is only 1.20 s, indicating that this method can effectively improve the efficiency of personalised classification of brand promotion information.
    Keywords: k-means algorithm; information attribute division; feature extraction; personalised classification of information.
    DOI: 10.1504/IJITM.2022.10052258
  • Information Technology Governance in the Government Public Sector: a systematic mapping of the scientific production   Order a copy of this article
    by Aline Sengik, Guilherme Lunardi 
    Abstract: The technological dependence and the increase of investments made in IT by the public sector have become IT Governance (ITG) a necessity for these organizations to increase their public value. So, based on a Systematic Literature Review, we aimed to map the research carried out on ITG in the government public sector. A total of 48 articles published in the databases Scopus and Web of Science comprise this studys portfolio. The results showed that the topic has gained relevance, especially in recent years, evidencing it as a research area in development and with opportunities for application in different organizations linked to the public area. Four themes stood out in the analyzed studies: ITG mechanisms, critical success factors, ITG models, and ITG focus areas. In addition, we provide information to guide a future research agenda on ITG in the public sector, thus expanding the existing body of knowledge on the topic.
    Keywords: IT Governance; Public Sector; Systematic Mapping; Government.

  • An analysis of the research trends and collaboration strategy of university and industry on digital twin technology   Order a copy of this article
    by Sang Young Park, Su Sung Kim, Sungjoo Lee 
    Abstract: The rapid transition to digitalize of all processes in the manufacturing field has promoted the introduction of new manufacturing technologies. However, few have focused on how to create synergies in R&D through universityindustry collaboration (UIC) in the relevant fields. To fill the research void, in this study, we analyzed the research trends of industry and university with a focus on the technological field of digital twins (DTs), which has recently attracted attention, and we explored partner selection for effective UIC strategies in the field. The results suggest that the university focuses on intelligent production systems using AI technology, whereas the industry focuses on industrial digitalization platforms for condition monitoring and predictive maintenance. The methodology proposed in this study can be applied to other industrial fields that require the UIC strategy. The research findings provide useful guidelines concerning UIC for industry practitioners and academic researchers in DTs.
    Keywords: digital twin; university–industry collaboration; type of collaboration; text mining; bibliometric analysis; portfolio analysis.

Special Issue on: Artificial Intelligence and Big data used on Business Management

  • Optimization of Outlier Data Mining Algorithm for Large Dataset Based on Unit   Order a copy of this article
    by Yizhi Li, Xiangming Zhou 
    Abstract: The purpose of this paper is to study and optimize the cell-based outlier data mining algorithm for large data sets, and further improve the profit group data mining algorithm. In this experiment, within the scope of the experiment, first use mathematical statistical analysis to investigate the unit-based large data set outlier data mining algorithm optimization, the data mining proportion of each category of the Internet of Things; and then use the data statistical method to classify statistical analysis and detect unit-based Of outlier data mining optimization algorithms for large data sets, research the outlier data mining speed of the optimized algorithm, study the ability to obtain outliers after optimization algorithm; and use a single variable method to compare and investigate distance-based outlier data mining algorithms and cell-based large data sets Speed comparison of outlier data mining algorithm optimization; Finally, use big data analysis method to fit and analyze the data.
    Keywords: Key words: Outlier Data; Algorithm Optimization; Big Data Set; Intelligent Internet of Things; Mining Speed.

  • Scheduling and Monitoring on Engineering Vehicles Based on IoTs   Order a copy of this article
    by Li Wan 
    Abstract: with the development of information technology, the traditional logistics industry meets a new opportunity in information time. In this paper, we design an intelligent logistics platform based on Internet of Things (IoTs). The locations and dynamic data of vehicles are collected using intelligent equipment on the platform. Moreover, we build a mathematical model based on collected data for a transportation problem, which is solved by Lingo to intend to obtain an intelligent schedule. Our solution of scheduling and monitoring of engineering vehicles based on IoTs can help enterprises reduce operational cost and improve service quality.
    Keywords: Scheduling1;Monitoring2;Engineering3;Vehicles4;IoT5;Intelligent6;Logistics7;logistics8; service9,etc.

  • Precision Advertising and Optimization Strategy Based on Big Data Algorithm   Order a copy of this article
    by Hailan Pan, Shi Yin 
    Abstract: This article first proposes the reference method, the case study method, the data analysis method and the comparison method, to estimate and approximate the quantitative analysis of the placement of high-precision data advertisements, and to clarify the current situation and characteristics of the accuracy of big data. Secondly, the advertising platform discussed in this article is an independent B/S system written in Java to follow J2EE specifications. The entire platform includes a back-end system that maintains advertising materials and advertising, a user behavior collection system, user behavior analysis, and recommendations for advertising based on interest. system. For third-party sites to use this system, they only need to access the Client Js Library, and the corresponding advertisements will be automatically pushed to the front-end page when consumers browse the page.
    Keywords: Big Data Algorithm;Accurate Advertising;Optimization Strategy;Collaborative Filtering Algorithm.

  • Parallel machine scheduling optimization based on improved multi-objective artificial bee colony algorithm   Order a copy of this article
    by Li Jun Yang 
    Abstract: Aiming at the scheduling model of the same kind of machine, considering that low carbon emission is an urgent problem to be solved in the manufacturing industry, a mathematical model containing the maximum completion time and maximum processing energy consumption was established. In order to balance the local development ability and global search ability of artificial bee colony algorithm, and improve the convergence speed of the algorithm, a scheduling optimization method of parallel machine based on improved multi-objective artificial bee colony algorithm was proposed. Firstly, a chaotic image initialization method is proposed to ensure the diversity and excellence of the initial population. Then, the individual threshold is used to dynamically adjust the search radius to improve the search accuracy and convergence speed. Finally, considering the development times of the external archive solution, the evolution is guided by selecting the elite solution reasonably. In order to verify the effectiveness of the algorithm, comparative experiments and performance analysis of the algorithm are carried out on several examples. The results show that the proposed algorithm can solve the scheduling problem of the same kind of machine effectively in practical scenarios.
    Keywords: parallel1;machine2;artificial3;bee4;colony5;scheduling6;optimization7;multi-objective8,etc.

  • Application of multi-channel attention mechanism in text classification of new media   Order a copy of this article
    by Wei-Xian Wang 
    Abstract: Sentiment analysis of new media text has become a research hotspot in recent years. In order to more effectively analyze the emotional polarity of new media text, this paper proposes a text classification algorithm based on multi-channel attention mechanism. First, channels based on bidirectional gating recurrent unit (BiGRU) neural network are used to extract semantic features, while channels based on fully connected neural network are used to extract emotional features. In order to extract the key information better, the attention mechanism is introduced into the two channels respectively, and the bidirectional encoder representation technology based on converter is used to provide the word vectors. Then, the real emotional semantics are embedded into the model through the dynamic adjustment of the word vector by the context. Finally, the semantic features and emotional features of the double channels are fused to obtain the final semantic expression. In the experiment part, NLPCC2014 data set and microblog data captured by crawler are used for comparative experiment. The experimental results show that the proposed multi-channel attention mechanism method can enhance the ability of emotion semantic capture, improve the performance of emotion classification.
    Keywords: new1,media2,sentiment3,analysis4,multi-channel5,attention6,mechanism7,etc.

  • Innovation and Entrepreneurship Orientation and Suggestions for New Engineering Computer Majors under the Background of Artificial Intelligence   Order a copy of this article
    by Hongjun Jia 
    Abstract: This article uses a variety of methods such as literature data method, questionnaire survey method, mathematical statistics, etc. to design an experiment on innovation and entrepreneurship education for new engineering computer under the background of artificial intelligence, to provide directions and suggestions for innovative and entrepreneurial talents in modern society and industry needs. This article has analyzed the current problems in the computer major training program in colleges and universities, and has mastered the degree of understanding, difficulties and needs of computer students on innovation and entrepreneurship. Among the interviewees, 51.63% of students think that starting a business is a good choice, and 23.48% of students are starting a business or have experienced a business. Among the needs of computer professional employability, the frequency of selection of practical ability and innovation ability is as high as 97.59% and 95.58%, which is enough to show the importance of practical ability and innovation ability.
    Keywords: Artificial Intelligence;Computer Science;New Engineering;Innovation and Entrepreneurship.

  • A prediction and analysis model of complex system based on extension neural network-Taking the prediction and analysis of terrorist events in the big data environment as an example   Order a copy of this article
    by Yachun Tang 
    Abstract: Predictive analysis of terrorist incidents often exists problems such as large data volume, large data types, large redundancy, and difficulty in dealing with multiple constraints, making it difficult to obtain effective prediction results for terrorist event prediction. Therefore, the methods of analyzing and predicting terrorist events in big data environment are analyzed, and a big data prediction analysis model for terrorist events based on improved extension neural network is presented. Firstly, a meta-element model for predicting and analyzing terrorist events is established, the preliminary cluster analysis of the terrorist event prediction analysis based on the extension transformation is performed, and a terror event prediction extension principal component analysis model is established; Then, based on the prediction of the extension principal component analysis in the terrorist incident, an improved extension neural network structure is constructed, and the extension neurons based on different extensions are established; Furthermore, the predictive analysis model of the terrorist network extension neural network is formed, and the corresponding model and algorithm implementation steps are given. Finally, the algorithm and the model are illustrated and compared by specific case analysis, which shows the validity and feasibility of the model and algorithm.
    Keywords: Predictive analysis model1; Extension neural network2; Extension theory3; Big data4; Terrorist incidents5,etc.

  • Embedded System Architecture-Computer Embedded Software Defect Prediction Based on Genetic Optimization Algorithm   Order a copy of this article
    by Aiju Wang 
    Abstract: With the rapid development of electronic measurement technology, people have put forward higher requirements for the diversity of oscilloscope functions and abundant peripheral interfaces. This paper aims to use genetic optimization algorithm to detect embedded software defects, provide users with prepared defect information, and improve the efficiency and accuracy of detection. This paper proposes popular algorithms for moving target video detection, three genetic optimization algorithms, selection operator, crossover operator, and mutation operator, and establishes a complete system, deepening a virtual simulation environment for embedded software development development model. In addition, from hardware simulation to the detection of software defects such as memory leaks and uninitialized variables, they are all included in the system and run through the entire process of embedded software development.
    Keywords: Embedded system architecture; genetic optimization algorithm; computer embedded; software defect.

  • O2O Customer Big Data Analysis System Based on Embedded Technology   Order a copy of this article
    by Fan Zhang, Yu Su 
    Abstract: The application of data acquisition systems is extremely common. People use data acquisition systems to obtain many signals that they need. However, for the special group of customers, data analysis is still needed. In order to study the operability of the new embedded technology applied to big data analysis, this article uses big data query method, system construction method and data export method to collect samples, analyze the data analysis collection system, and streamline the algorithm. And then it will create a system that can analyze customer big data. If you want to analyze, you have to collect first. In the introduction of the method of collecting data in the article, first calculate the absolute error of a single measurement. The analysis results of the two gauge blocks of 1.0mm and 1.1mm show that when 4 measurements are taken, the absolute errors of the results are all less than 0.5um.
    Keywords: Embedded Technology; O2O Customers Introduction; Big Data Technology; Data Mining; Analysis System.

  • Research on Application of Optimal Particle Swarm Optimization Algorithm in Logistics Route Improvement   Order a copy of this article
    by Xianyu Wang 
    Abstract: Aiming at the logistics path optimization model, the author converts the logistics path optimization problem into a classical traveling salesman problem in the field of mathematics. The adaptive particle swarm optimization algorithm is used to dispose of the model problem. In the algorithm, each particle has four behavior evolution strategies, and the individual speed and position are updated by selecting the strategy with the highest probability. An adaptive particle swarm optimization algorithm is proposed. The algorithm improves the speed of individual optimization by using probabilistic mutation algorithm of policy behavior, which avoids falling into local optimal solution. For the purpose of demonstrating the effectiveness and performance of the method, comparative experiments are conducted on the open source Oliver30 data set. Experimental results show that the average path length achieved by the proposed method is closer to the optimal value, and the convergence speed is fast.
    Keywords: convergence1;particle2;swarm3;optimization4;adaptive5;multi-strategy6,etc.

  • Artificial Intelligence and Big Data in the Production Process Optimize the Parameters of the Cut Tobacco Making Process   Order a copy of this article
    by Wenlong Jin, Liujing Wang, Chengting Zhang 
    Abstract: This article aims to use big data and artificial intelligence energy systems to optimize the parameters in the cut tobacco making process. This paper designs a production process detection system based on artificial intelligence, and uses a database to store big data. It analyzes the data through the database, and selects an important step in the cut tobacco making process, and optimizes the parameters of threshing and redrying. The speed of beater, hot air temperature and moisture regain temperature in the threshing and redrying process were compared and analyzed. Finally, the leaf emergence rate and stem emergence rate are compared between the tobacco shreds with optimized parameters and the unoptimized shredded tobacco. The results show that the optimized parameters are 550, hot air temperature 90
    Keywords: Artificial Intelligence and Big Data; Cut Tobacco Making Process; Parameter Optimization; Optimization Process.

  • Design and Implementation of Corporate Governance Automated Decision Model based on WEB Data   Order a copy of this article
    by Qiang Ma 
    Abstract: As a global, dynamic interactive and cross platform distributed graphic information system based on the hypertext and HTTP, WEB provides a graphical and easy to access intuitive interface for users to find information, and organizes the information nodes into an interrelated network structure. In corporate governance, information mining and processing is particularly important. WEB data can provide a large amount of high-dimensional structured and unstructured data for corporate decision-makers to make more effective management decisions. Therefore, this paper attempts to build a model to help managers improve their decision-making ability and corporate governance. Through the integration of WEB data, this paper tests the impact of executives ME on perks. The results show that the background characteristics of executives could affect their decision-making, and the model can be established under certain constraints. This paper enriches the application of decision-making model to improve the level of corporate governance.
    Keywords: WEB; Automated Decision; Corporate Governance; Decision Support System; Marketization.

  • Development of Regional Agricultural E-commerce in China Based on CAS   Order a copy of this article
    by Bahao Li 
    Abstract: The paper makes a exploration of the mode of agricultural production and operation. Based on the analysis of the demand of the farmers to the e-commerce system, the intelligent sensor is connected to the Internet of things, attempt to propose the solution of \"Internet plus agricultural business\" is put forward. main regional agricultural products in China are aquatic products. In 2016, the total output of aquatic products was 2.853 million tons, of which the output of pelagic fishery and mariculture were 449000 tons and 167000 tons respectively. The total agricultural output value of crops ranked second, including 152000 tons of grain, 37000 tons of vegetables and 24000 tons of fruits. E-commerce of agricultural products effectively introduces e-commerce into traditional trade of agricultural products and realizes the organic combination of the two. It avoids the shortcomings of the traditional agricultural products trading system.
    Keywords: Intelligent Systems; Regional Agricultural Products; E-commerce Development; Intelligent Sensors.

  • Design and Implementation of Big Data Analysis and Visualization Platform for Smart City   Order a copy of this article
    by Kai Sun, Naidi Liu, Xinghua Sun, Yuxin Zhang 
    Abstract: Through the construction of smart cities, the modernization of urban governance systems and governance capacities can be improved. However, the constructions of smart cities face the challenges of data failure, lack of relevance, information fragmentation, fundamental data without accepting new data and innovative concepts. Business big data are extracted, converted from different departments, and these structured, semi-structured, and non-structured data are extracted and transformed to load in data warehouse by ETL. Through the data sharing and exchange platforms, then use joint databases and element searches to create Multi-department data business views to support specific applications in smart cities. This research realizes the smart city big data visual analysis system. The system architecture includes data access layer, data management layer, data analysis layer and release management layer. The system mainly includes four modules: People's livelihood service, citizen big data, urban operation and big data map. This system helps break data barriers, connect data islands, and digitize many municipal businesses, so as to perform data analysis and data visualization, and provide support for refined governance decision-making. The system realizes the access, integration, transformation, visualization and interactive decision analysis of various data of urban life.
    Keywords: Smart City; Big Data; Urban Governance; Data Visualization.

  • Real-time Prediction Algorithm and Simulation of Sports Results Based on Internet of Things and Machine Learning   Order a copy of this article
    by Yibing Ma, Hongyu Guo, Yuqi Sun, Fang Liu 
    Abstract: In the field of sports prediction, the prediction results must be processed, because many events in large-scale sports events are linked to funds. Through inquiries on the Internet, more and more sports-related data can be obtained. Using these data, people continue to develop intelligent models and prediction systems, optimize and innovate these models and systems, and then more accurately predict the results of the game. This article is mainly based on basketball technical time series statistics, using a three-layer feedforward back-propagation neural network, and adopting a rotation prediction method to predict the most important technical and statistical indicators of the team. According to the teams forecast data, the average field goal percentage is 46.03%, the 3-point field goal percentage is 37.48%, the assists are 12.95, and the backcourt rebounds are 25.4.
    Keywords: Machine Learning; Exercise Results; Real-Time Prediction; Internet of Things.

Special Issue on: LISS 2017 Emerging Trends, Issues and Challenges in Big Data and Its Implementation

  • Evolution of intellectual structure of data mining research based on keywords   Order a copy of this article
    by Yue Huang 
    Abstract: Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. We retrieved 5380 papers, published in 11 leading journals of data mining defined by Google Scholar, from SCIE under Web of Science and Scopus databases between 2007 and 2016 to carry out bibliometric analysis. As indicated by the analysis on the evolution of keyword frequency, the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Matrices of high-frequency keywords were also built for different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show there are four main data mining topics and the attention has been paid more to graph data mining and complex network analysis in the past 5 years.
    Keywords: data mining; intellectual structure; co-word analysis; clustering; evolution analysis.

  • The Effects of Relationship Quality and Knowledge Sharing on Service Innovation Performance: Organizational learning as a mediator   Order a copy of this article
    by Zhaoquan Jian, MOHAMED A.L.I. OSMAN, L.E.I. LI 
    Abstract: Drawing on RBV and service dominant logic and using data collected from 243 companies, this paper aims at examining the interplays between relationship quality, knowledge sharing, organizational learning, and service innovation performance. This empirical research found that knowledge sharing and relationship quality were significantly related to organizational learning, that in turn significantly affected service innovation performance. Moreover, better relationship quality would yield improved knowledge sharing. Furthermore, we propose that organizational learning is a significant mediator through which knowledge sharing influences firm performance, and that relationship quality is also a critical factor that facilitates service innovation.
    Keywords: Relationship Quality; Knowledge Sharing; Organizational Learning; Service Innovation Performance.