Forthcoming and Online First Articles

International Journal of Big Data Management

International Journal of Big Data Management (IJBDM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Big Data Management (2 papers in press)

Regular Issues

  • Diastema: Data-driven Stack for Big Data Applications Management and Deployment   Order a copy of this article
    by Athanasios Kiourtis, Yannis Poulakis, Panagiotis Karamolegkos, Andreas Karabetian, Konstantinos Voulgaris, Argyro Mavrogiorgou, Dimosthenis Kyriazis 
    Abstract: Most techniques for data processing run on standard infrastructure management systems, while large datasets is being increasingly generated. The main challenge refers to using technology to gather efficient and faster insights from a dataset, considering not asking what data is easily obtainable and which tools are amenable to working with that dataset, but rather what question the analysis is trying to answer. This creates a landscape with data-intensive projects that prioritise technical prowess of execution over the robustness of analytical findings. Hence, a data-driven stack for big data applications management and deployment is being described, diastema, bringing efficient data-as-a-service data management through distributed storage and analytics, aiming at high performance and utilisation of heterogeneous resources, including abstraction, gateways, and small-footprint virtual machines. Diastema is evaluated through training a customer forecasting model for indicating customers behaviour, turning limited-value raw data to timely, relevant data, targeting at business agility and competitiveness.
    Keywords: dynamic orchestration; infrastructure management; resources allocation; data-as-a-service.
    DOI: 10.1504/IJBDM.2023.10048598
     
  • Qualitative Study on Barriers of Adopting Big Data Analytics for UK SMEs   Order a copy of this article
    by Matthew Willetts, Anthony S. Atkins 
    Abstract: Big data analytics have been widely adopted by large companies to achieve competitive advantage. However, small and medium-sized enterprises (SMEs) are underutilising this technology due to the existence of a number of barriers to adoption including financial constraints and lack of information. Previous research identified 69 barriers to SMEs adoption of big data analytics, rationalised to 21 barriers categorised into pillars based on theoretical frameworks. The barriers identified through the research were validated quantitatively, through a survey and also qualitatively, through semi-structured interviews with UK SME representatives. This paper describes the qualitative validation of the barriers to SME adoption of big data analytics and discusses how these barriers were incorporated into an SME big data adoption framework.
    Keywords: big data analytics; SMEs; big data analytics barriers; strategic framework.
    DOI: 10.1504/IJBDM.2024.10052988
     

 

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