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Data Technologies and Applications
Number of Followers: 337  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2514-9288 - ISSN (Online) 2514-9288
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  • Head motion coefficient-based algorithm for distracted driving detection
    • Pages: 171 - 188
      Abstract: Data Technologies and Applications, Volume 53, Issue 2, Page 171-188, April 2019.
      Purpose Concentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction. Design/methodology/approach The system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering. Findings The accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames. Originality/value The system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-07T10:21:54Z
      DOI: 10.1108/DTA-09-2018-0086
       
  • Predicting drug–disease associations by network embedding and
           biomedical data integration
    • Pages: 217 - 229
      Abstract: Data Technologies and Applications, Volume 53, Issue 2, Page 217-229, April 2019.
      Purpose The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue. Design/methodology/approach Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation. Findings This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs. Originality/value This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-07T10:22:18Z
      DOI: 10.1108/DTA-01-2019-0004
       
  • The influence of the diffusion of food safety information through social
           media on consumers’ purchase intentions
    • Pages: 230 - 248
      Abstract: Data Technologies and Applications, Volume 53, Issue 2, Page 230-248, April 2019.
      Purpose The purpose of this paper is to investigate how the diffusion of food safety information through social media affects customers’ purchase intentions in China. This leads to the identification of the critical factors that impact the purchase intention of individual consumer through the diffusion of food safety information using social media in China. Design/methodology/approach A research model is proposed based on a comprehensive review of the related studies. Such a model is then tested and validated using structural equation modeling based on the survey of 199 individuals who have experience in purchasing food products online while having social media accounts. Findings The study reveals that friend recommendation and perceived risk directly affect consumers’ purchase intentions and opinion leader recommendation, quality of information, credibility of information and demand for information indirectly affect consumers’ purchase intentions through the diffusion of food safety information using social media in China. Originality/value This study is the first of this kind in China for exploring the critical factors that affect consumers’ purchase intentions through the diffusion of food safety information using social media. The findings of the study are significant for the government and food enterprises to make a full use of the advantages of social media to improve the communication of food safety information in ensuring the safety of the food supply in China.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-07T10:22:02Z
      DOI: 10.1108/DTA-05-2018-0046
       
  • Collaborative knowledge management for corporate ecological responsibility
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose Knowledge has become the basis of enhancing the core competitiveness of enterprises in this era of knowledge-driven economies. Collaborative knowledge management not only realizes the real-time exchange and communication of knowledge among different enterprises, but also facilitates the collaboration and integration of knowledge. Collaborative knowledge management has been successfully applied to different fields. To address the poor ecological responsibility of enterprises, the purpose of this paper is to introduce the concept of collaborative knowledge management in this research to determine if the evolution of the decision-making process in collaborative knowledge management is involved in corporate ecological responsibility (CER). Design/methodology/approach This research established an evolutionary game model of collaborative knowledge management for CER. The behavioral, evolutionary law and stable behavioral, evolutionary strategy of the participants was identified according to the replicator dynamics equation. Simulation analysis was conducted using MATLAB software. Findings Research results demonstrated that, first, the strategic selection of firms is influenced by cost and interest coefficients. Second, the strategy, selection of enterprises, is related to the common benefits of enterprise cooperation. Third, during the systematic evolution and stabilization of strategies, enterprises adopt the same knowledge strategies. Originality/value On the basis of the research findings, policy suggestions were proposed to encourage enterprises to implement collaborative knowledge management strategies in ecological responsibility.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-14T10:42:26Z
      DOI: 10.1108/DTA-01-2019-0003
       
  • Grammar checking and relation extraction in text: approaches, techniques
           and open challenges
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The purpose of this paper is to provide an overall review of grammar checking and relation extraction (RE) literature, their techniques and the open challenges associated with them; and, finally, suggest future directions. Design/methodology/approach The review on grammar checking and RE was carried out using the following protocol: we prepared research questions, planed for searching strategy, addressed paper selection criteria to distinguish relevant works, extracted data from these works, and finally, analyzed and synthesized the data. Findings The output of error detection models could be used for creating a profile of a certain writer. Such profiles can be used for author identification, native language identification or even the level of education, to name a few. The automatic extraction of relations could be used to build or complete electronic lexical thesauri and knowledge bases. Originality/value Grammar checking is the process of detecting and sometimes correcting erroneous words in the text, while RE is the process of detecting and categorizing predefined relationships between entities or words that were identified in the text. The authors found that the most obvious challenge is the lack of data sets, especially for low-resource languages. Also, the lack of unified evaluation methods hinders the ability to compare results.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-14T10:40:46Z
      DOI: 10.1108/DTA-01-2019-0001
       
  • Performance assessment and major trends in open government data research
           based on Web of Science data
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The purpose of this paper is to evaluate the global progress and explore research areas and development trends of open government data (OGD) field from the Web of Science (WOS) database by applying the bibliometric visualization approach. Design/methodology/approach This paper conducted a bibliometric mapping study on OGD scientific research publications based on WOS from six aspects. Findings There are six research perspectives on OGD research. European countries and developed countries pay more attention to OGD movement. The 20 most cited and highly influential research documents were identified. What’s more, the analysis of journals level highlights the interdisciplinary and cross-disciplinary characteristics of OGD research. Current six research topics for OGD research that have been formed and two major emerging research priorities in OGD research fields were identified. Research limitations/implications The limitation is that data retrieval result which decided to include only 180 publications in the WOS-indexed publications produced a bias against research publications published in non-WOS publication sources. A fuller research trend would be obtained with the more extensively used electronic databases. Practical implications By dint of bibliometric analysis, this paper may be able to quantify research patterns on OGD, to analyze what has been done in this field and to identify the main research hotspots. Therefore, it can aid academic researchers and practicing professionals in contributing to the field more effectively and advancing scientific progress in the field of OGD research. Social implications The results can also promote the study on OGD movement in academia, government and industry and also enrich the theory of OGD and provide some new perspectives for research on OGD. Originality/value This is the first study to quantify and evaluate global research patterns and development trends in OGD research based on WOS database, which provides a quantitative perspective on OGD studies that may assist in advancing the development of the field.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-14T10:36:26Z
      DOI: 10.1108/DTA-10-2017-0078
       
  • Multiple patent network analysis for identifying safety technology
           convergence
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose Using the large database of patent, the purpose of this paper is to structure a technology convergence network using various patent network analysis for integrating different results according to network characteristics. Design/methodology/approach The patent co-class analysis and the patent citation analysis are applied to discover core safety fields and technology, respectively. In specific, three types of network analysis, which are centrality analysis, association rule mining analysis and brokerage network analysis, are applied to measure the individual, synergy and group intensity. Findings The core safety fields derived from three types of network analysis used by different nature of data algorithms are compared with each other to understand distinctive meaning of cores of patent class such as medical safety, working safety and vehicle safety, differentiating network structure. Also, to be specific, the authors find the detailed technology contained in the core patent class using patent citation network analysis. Practical implications The results provide meaningful implications to various stakeholders in organization: safety management, safety engineering and safety policy. The multiple patent network enables safety manager to identify core safety convergence fields and safety engineers to develop new safety technology. Also, in the view of technology convergence, the strategy of safety policy can be expanded to collaboration and open innovation. Originality/value This is the initial study on applying various network analysis algorithms based on patent data (class and citation) for safety management. Through comparison among network analysis techniques, the different results are identified and the collective decision making on finding core of safety technology convergence is supported. The decision maker can obtain the various perspectives of tracing technology convergence.
      Citation: Data Technologies and Applications
      PubDate: 2019-06-14T10:25:06Z
      DOI: 10.1108/DTA-09-2018-0077
       
  • Designing of smart chair for monitoring of sitting posture using
           convolutional neural networks
    • First page: 142
      Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used. Findings The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children. Originality/value This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.
      Citation: Data Technologies and Applications
      PubDate: 2019-02-28T12:58:16Z
      DOI: 10.1108/DTA-03-2018-0021
       
  • Role of social anxiety on high engagement and addictive behavior in the
           context of social networking sites
    • First page: 156
      Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The purpose of this paper is to tackle the problem of technology addiction by investigating the differences between the antecedences of addictive (problematic technology usage) and high-engagement behavior (non-problematic technology usage). The case of social networking site usage (SNS, e.g. Facebook, Instagram or Twitter) is taken as the case out of the reason of prevalent user population. Design/methodology/approach It is revealed that people tend to use SNS not only for building a relationship, but also for communicating. In other words, there are inner needs of adopting the SNS technology. However, no clear definitions can be followed for determining the problematic SNS usage, addictive behavior and the high-engagement behavior. Therefore, this study adopts the notion of uses and gratification theory (U&G theory) for investigating the SNS usage behavior. Also, the social anxiety is also first introduced to integrate into the research for an empirical study. Findings Results reveal that gratification sought and relationship maintenance are associated with the addictive behavior, whereas the relationship maintenance is significantly related to high-engagement behavior. Research limitations/implications First, the selected data represents a sample of SNW users in the Asian Pacific region and mainly from the group of young college users. Therefore, caution must be taken when generalizing the findings to other SNW users or groups. Second, the time aspect related to social media dependence may need to be considered in future studies. Third, the authors found marginal support for the influence of intentions of high engagement¸ and future studies may consider applying other theories that could better explain these types of behavior. Practical implications The results of this study provide strong evidence that inner anxiety perceived by users should not be neglected while tackling the problematic internet use due to SNW addiction because it can strengthen the force for depending on SNW for seeking social support. Apart from the value of perceived enjoyment as asserted in previous studies, this study opens up a new opportunity to tackle SNW dependence. Social implications The key implication of this research is that the impact of the mental health of users on SNW problematic should not be overlooked . The higher the level of anxiety perceived, the more likely is the SNW dependence. Therefore, the online behavior depending on psychological health should be addressed because it may be a critical point for assisting users to adopt SNW wisely. Originality/value This study confirms that social anxiety people experience in real (offline) life has impacts on online behavior of SNS usage (online). It suggests that the difference between users as the perceived level of social anxiety can trigger different levels of SNS usage. Second, U&G theory is proven valid in understanding SNS addiction. Third, relationship maintenance through the use of SNS reveals its dissimilar effects on SNS addiction and high engagement.
      Citation: Data Technologies and Applications
      PubDate: 2019-03-29T11:21:01Z
      DOI: 10.1108/DTA-09-2018-0076
       
  • Adaptation algorithms for selecting personalised learning experience based
           on learning style and dyslexia type
    • First page: 189
      Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose Through harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type. Design/methodology/approach The DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases. Findings An experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes. Originality/value The DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.
      Citation: Data Technologies and Applications
      PubDate: 2019-04-17T09:55:00Z
      DOI: 10.1108/DTA-10-2018-0092
       
  • An analysis of academic librarians competencies and skills for
           implementation of Big Data analytics in libraries
    • First page: 201
      Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The purpose of this paper is to analyze the views and capabilities of librarians for the implementation of Big Data analytics in academic libraries of Pakistan. The study also sets out to check the relationship between the required skills of librarians and the application of Big Data analytics. Design/methodology/approach A survey was conducted to gather the required data from the targeted audience. The targeted population of the study was Head/In charge library managers of Pakistani university libraries, which were 173 in total. All the respondents (academic librarians) were invited through an e-mail to respond to the survey voluntarily. Out of 173 respondents from higher education commission of Pakistan chartered university libraries, 118 librarians (68.2 percent) completed the survey that was finally considered, and after checking data, recommendation for analysis was made. To analyze the collected data, statistical technique Pearson correlation was applied using statistical package for social science version 25 to know the strength of the mutual correlation of variables. Findings The findings of the study show a strong correlation between the required competencies and skills of librarians for the implementation of Big Data analytics in academic libraries. In all variables of the study, the correlation was highly significant, except two of the variables, including “concept of Big Data” and “different forms of data.” The study also reveals that most of the respondents were well aware of the concept of Big Data analytics. Moreover, they were using a large amount of data to carry out various library operations, including the acquisition, preservation, curation and analysis of data. Originality/value This study is significant in the sense that it fills a substantial gap in the literature regarding the perspective of librarians on Big Data analytics.
      Citation: Data Technologies and Applications
      PubDate: 2019-03-29T11:25:39Z
      DOI: 10.1108/DTA-09-2018-0085
       
 
 
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