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Data Technologies and Applications
Journal Prestige (SJR): 0.355
Citation Impact (citeScore): 1
Number of Followers: 323  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2514-9288
Published by Emerald Homepage  [356 journals]
  • M-banking barriers in Pakistan: a customer perspective of adoption and
           continuity intention
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The purpose of this paper is to determine barriers jeopardizing the adoption and usage intention of mobile banking (M-banking) in Pakistan and provide deeper insights to fix such deteriorating factors. Design/methodology/approach Data was collected in countrywide regional headquarters to mark the utmost generalizability of the results, which included seven largest cities of Pakistan. SEM path analysis was used to analyze data collected from Pakistan’s top 5 bank customers incorporating both users and non-users. Findings Results revealed that lack of awareness, initial trust and compatibility and perceived risk were the core barriers that stood out as obstacles to the adoption and usage of M-banking in Pakistan. It was also approved that having fixed these core barriers would outcome in existing users’ continuity intent besides raising new users’ inclination toward M-banking. Originality/value The study has unveiled the core barriers that have so far impeded the adoption and usage of M-banking. There is not a unified position concerning adoption and usage blockades. Factors differ with contexts, markets, time and kinds of innovations. However, this study is unlike past studies that merely studied students within a specified institute in a restricted jurisdiction. This is the first study to have nationally explored adoption and usage issues; thus, it is anticipated to potentially contribute to the prevailing literature especially in Pakistani context where a few studies prevail, addressing M-banking adoption and usage barriers.
      Citation: Data Technologies and Applications
      PubDate: 2019-02-07T03:01:48Z
      DOI: 10.1108/DTA-04-2018-0022
       
  • Parallelization and analysis of selected numerical algorithms using OpenMP
           and Pluto on symmetric multiprocessing machine
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose In recent years, there is a gradual shift from sequential computing to parallel computing. Nowadays, nearly all computers are of multicore processors. To exploit the available cores, parallel computing becomes necessary. It increases speed by processing huge amount of data in real time. The purpose of this paper is to parallelize a set of well-known programs using different techniques to determine best way to parallelize a program experimented. Design/methodology/approach A set of numeric algorithms are parallelized using hand parallelization using OpenMP and auto parallelization using Pluto tool. Findings The work discovers that few of the algorithms are well suited in auto parallelization using Pluto tool but many of the algorithms execute more efficiently using OpenMP hand parallelization. Originality/value The work provides an original work on parallelization using OpenMP programming paradigm and Pluto tool.
      Citation: Data Technologies and Applications
      PubDate: 2019-02-07T03:01:44Z
      DOI: 10.1108/DTA-05-2018-0040
       
  • Applying big data analytics to support Kansei engineering for hotel
           service development
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining. Design/methodology/approach The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships. Findings The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results. Originality/value Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.
      Citation: Data Technologies and Applications
      PubDate: 2019-02-06T11:02:17Z
      DOI: 10.1108/DTA-05-2018-0048
       
  • Towards semantically-aided domain specific business process modeling
    • Pages: 463 - 481
      Abstract: Data Technologies and Applications, Volume 52, Issue 4, Page 463-481, September 2018.
      Purpose Domain-specific process modeling has been proposed in the literature as a solution to several problems in business process management. The problems arise when using only the generic Business Process Model and Notation (BPMN) standard for modeling. This language includes domain ambiguity and difficult long-term model evolution. Domain-specific modeling involves developing concept definitions, domain-specific processes and eventually industry-standard BPMN models. This entails a multi-layered modeling approach, where any of these artifacts can be modified by various stakeholders and changes done by one person may influence models used by others. There is therefore a need for tool support to keep track of changes done and their potential impacts. The paper aims to discuss these issues. Design/methodology/approach The authors use a multi-context systems-based approach to infer the impacts that changes may cause in the models; and alsothe authors incrementally map components of business process models to ontologies. Findings Advantages of the framework include: identifying conflicts/inconsistencies across different business modeling layers; expressing rich information on the relations between two layers; calculating the impact of changes taking place in one layer to the rest of the layers; and selecting incrementally the most appropriate semantic models on which the transformations can be based. Research limitations/implications The authors consider this work as one of the foundational bricks that will enable further advances toward the governance of multi-layer business process modeling systems. Extensive usability tests would enable to further confirm the findings of the paper. Practical implications The approach described here should improve the maintainability, reuse and clarity of business process models and in extension improve data governance in large organizations. The approaches described here should improve the maintainability, reuse and clarity of business process models. This can improve data governance in large organizations and for large collections of processes by aiding various stakeholders to understand problems with process evolutions, changes and inconsistencies with business goals. Originality/value This paper fulfills an identified gap to enabling semantically aided domain–specific process modeling.
      Citation: Data Technologies and Applications
      PubDate: 2018-08-14T02:26:45Z
      DOI: 10.1108/DTA-01-2018-0007
       
  • Topological and topical characterisation of Twitter user communities
    • Pages: 482 - 501
      Abstract: Data Technologies and Applications, Volume 52, Issue 4, Page 482-501, September 2018.
      Purpose Most of the existing literature on online social networks (OSNs) either focuses on community detection in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. The purpose of this paper is to characterise the semantic cohesion of such groups through the introduction of new measures. Design/methodology/approach A theoretical model for social links and salient topics on Twitter is proposed. Also, measures to evaluate the topical cohesiveness of a group are introduced. Inspired from precision and recall, the proposed measures, called expertise and representativeness, assess how a set of groups match the topic distribution. An adapted measure is also introduced when a topic similarity can be computed. Finally, a topic relevance measure is defined, similar to tf.idf (term-frequency, inverse document frequency). Findings The measures yield interesting results, notably on a large tweet corpus: the metrics accurately describe the topics discussed in the tweets and enable to identify topic-focused groups. Combined with topological measures, they provide a global and concise view of the detected groups. Originality/value Many algorithms, applied on OSN, detect communities which often lack of meaning and internal semantic cohesion. This paper is among the first to quantify this aspect, and more precisely the topical cohesion and topical relevance of a group. Moreover, the proposed indicators can be exploited for social media monitoring, to investigate the impact of a group of people: for instance, they could be used for journalism, marketing and security purposes.
      Citation: Data Technologies and Applications
      PubDate: 2018-08-17T09:57:32Z
      DOI: 10.1108/DTA-01-2018-0006
       
  • Selection methods and diversity preservation in many-objective
           evolutionary algorithms
    • Pages: 502 - 519
      Abstract: Data Technologies and Applications, Volume 52, Issue 4, Page 502-519, September 2018.
      Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.
      Citation: Data Technologies and Applications
      PubDate: 2018-08-23T10:49:02Z
      DOI: 10.1108/DTA-01-2018-0009
       
  • Towards a common and semantic representation of e-portfolios
    • Pages: 520 - 538
      Abstract: Data Technologies and Applications, Volume 52, Issue 4, Page 520-538, September 2018.
      Purpose Since the early 1980s, a paradigm shift, caused by the work undertaken in the field of cognitive psychology, has occurred. This shift is known as the move from teacher-centered instruction to learner-centered or learning-centered instruction, and emphasizes the importance of building new knowledge on previous ones, interacting with peers, making meaningful and reflective learning and being engaged in his own path to foster learning. This new vision of teaching has created a need for new learning and assessment instruments that are better adapted to these pedagogical realities. In this context, the electronic portfolio or e-portfolio is one of the most versatile and effective tools that have been proposed for this purpose. More specifically, the interest in e-portfolios has grown considerably with the emergence of the competency-based approach and portfolio-based competency assessments. The purpose of this paper is to describe a semantic-based representation of e-portfolios, defined on the basis of official e-portfolio standards and specifications. Moreover, a comparative study of several well-known e-portfolio solutions has been carried out based on different facets, such as functional features, technical and organizational features. The objective is to identify those features that are mostly supported by e-portfolio solution providers and accordingly to gain a fairly accurate idea of the common structure of e-portfolios. In addition, the authors take advantage of an already implemented ontological model describing competency-related characteristics of learners and learning objects and combine it with the e-portfolio ontology, with a view to support a more reliable and authentic competency assessment. Design/methodology/approach The proposed e-portfolio ontology was built following the ontology development methodology Methontology (Fernandez et al., 1997). In addition, it was constructed using the Protégé ontology environment (Protégé, 2007) and was implemented in OWL (Web Ontology Language) (Antoniou and Harmelen, 2004). Findings The proposed e-portfolio ontology provides humans with a shared vocabulary that enables capturing the most important elements in e-portfolios and serves as the basis for the semantic interoperability for machines. Originality/value The main advantage of the e-portfolio ontology lies in its ability to provide a common and semantically enriched representation of e-portfolio artifacts, thus facilitating the interoperability and exchange of competency evidences between different learning systems and platforms. In addition, capturing the semantics of e-portfolios helps to make them utilizable by intelligent applications.
      Citation: Data Technologies and Applications
      PubDate: 2018-08-15T10:44:38Z
      DOI: 10.1108/DTA-01-2018-0008
       
  • Logical foundations of hierarchical model checking
    • Pages: 539 - 563
      Abstract: Data Technologies and Applications, Volume 52, Issue 4, Page 539-563, September 2018.
      Purpose The purpose of this paper is to develop new simple logics and translations for hierarchical model checking. Hierarchical model checking is a model-checking paradigm that can appropriately verify systems with hierarchical information and structures. Design/methodology/approach In this study, logics and translations for hierarchical model checking are developed based on linear-time temporal logic (LTL), computation-tree logic (CTL) and full computation-tree logic (CTL*). A sequential linear-time temporal logic (sLTL), a sequential computation-tree logic (sCTL), and a sequential full computation-tree logic (sCTL*), which can suitably represent hierarchical information and structures, are developed by extending LTL, CTL and CTL*, respectively. Translations from sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are defined, and theorems for embedding sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are proved using these translations. Findings These embedding theorems allow us to reuse the standard LTL-, CTL-, and CTL*-based model-checking algorithms to verify hierarchical systems that are modeled and specified by sLTL, sCTL and sCTL*. Originality/value The new logics sLTL, sCTL and sCTL* and their translations are developed, and some illustrative examples of hierarchical model checking are presented based on these logics and translations.
      Citation: Data Technologies and Applications
      PubDate: 2018-08-29T01:38:43Z
      DOI: 10.1108/DTA-01-2018-0002
       
  • A data-driven neural network architecture for sentiment analysis
    • Abstract: Data Technologies and Applications, Ahead of Print.
      Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.
      Citation: Data Technologies and Applications
      PubDate: 2018-12-14T03:37:57Z
      DOI: 10.1108/DTA-03-2018-0017
       
 
 
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