Forthcoming and Online First Articles

International Journal of Big Data Management

International Journal of Big Data Management (IJBDM)

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International Journal of Big Data Management (6 papers in press)

Regular Issues

  •   Free full-text access Open for Research Data Managers: A Primer
    ( Free Full-text Access ) CC-BY-NC-ND
    by Karen Payne, Chantelle Verhey 
    Abstract: Data managers are currently investigating the value proposition of (SDO) with a lightweight implementation that couples the SDO vocabulary with indexing Google Dataset Search. This pathway essentially webifies dataset search and syndication and enhances dataset discovery. The Primer was created to layout the SDO landscape, where it came from, what is driving its uptake in research data management, and how it works in broad strokes. It was designed to introduce individuals with little technical knowledge to the benefits and importance of and aimed to be a one-step-back from the numerous guidance documents that are being produced in the RDM community. This document describes the mark-up process in very simple terms, provides current methods of adoption by the research community, describes related technical areas relevant to data managers and lists what organisations they should follow to keep apprised of this work.
    Keywords:; SDO; interoperability; Google Dataset Search; GDSS; indexing; mark-up; research data managers; extensions; international technology office; semantics; ontologies; Semantic Web.
    DOI: 10.1504/IJBDM.2022.10048569
  • How Social Media Data Can Influence Consumers’ Attitudes towards Cosmetic Brands? The Case of Maybelline   Order a copy of this article
    by Rajibul Hasan, Mustafeed Zaman, Eloise Princet 
    Abstract: Social media data play a vital role in consumers brand awareness and their loyalty. However, little is known about consumers social media activities in the context of cosmetic brands. This research examines the impact of consumers social media activities and the use of social media data on their attitude of one cosmetic brand Maybelline. Analysing the survey data from 248 French consumers using SmartPLS software, we unexpectedly find: 1) consumers attitude towards online sponsored recommendations strongly influence their attitude towards a cosmetic brand; 2) both consumers online contributions on social media and their peers social influence positively influence their attitude towards a cosmetic brand. Results also suggest that on social media, consumers look for trustworthy information on cosmetic brands. Our results also highlight how cosmetic brands may optimise their social media data in order to create trustworthy contents on social media.
    Keywords: social media data; brand attitude; cosmetic brand; Maybelline; France.
    DOI: 10.1504/IJBDM.2021.10043324
  • The Role of Big Data Analytics in Personalized Servicers   Order a copy of this article
    by H. A. K. N. S. Surangi, Tharinya Sellathurai 
    Abstract: This paper qualitatively analyses how financial institutions in Sri Lanka can use the big data to optimise their offerings to give personalised services, focusing on the technological aspects needed. Respondents were part of the bank’s senior management, and they have readily involved in finance technologies. Findings revealed that personalisation is crucial for gaining a competitive advantage and acknowledging that the young techno-centric customers demand it. However, the key issues are legacy banking technology infrastructure, outdated statutory regulations that limit the deployment of advanced software tools and the organisational culture and perception when adopting new digital construction by the top management at the banks. This research is novel to the domain of big data in finance, thus serving as a pivotal contributor to knowledge and a guide to future research. Finally, suggestions to the stated difficulties and managerial implications guide the future implementation of such a technique in the banking industry.
    Keywords: banking sector; big data analytics; qualitative; case study; personalised services.

  • 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
  • Does the Use of Big Data Make the Websites More Innovative? : Evidence from the UAE   Order a copy of this article
    by Mustafeed Zaman, Noela Michael, Ian Michael, Rajibul Hasan 
    Abstract: Information and communication technologies allow companies to obtain massive amounts of data. These data, known as big data, bring new opportunities for companies and give them competitive advantages. Tourism and hospitality firms may use these data to forecast the demand, and propose more personalised services to visitors. This paper aims to explore whether the use of $$ can make websites more innovative in terms of service. An empirical approach is used to establish a structured framework of six evaluation criteria for websites innovativeness. Fuzzy logic is employed to objectively evaluate the perceived service-innovativeness and overall e-service quality of United Arab Emirates (UAE) destination marketing organisation (DMO) websites. The findings propose a ranking of the websites according to their perceived innovativeness, providing in-depth information on performance and forming criteria which will allow managers to understand the importance of different web/e-service functionalities, sharing insights concerning why some DMO websites are more innovative than others.
    Keywords: big data; website innovativeness; destination branding; DMO websites; UAE; fuzzy TOPSIS.
    DOI: 10.1504/IJBDM.2023.10048767
  • A Hybrid Neuro-Fuzzy Technique to Overcome Clustering Approach Issues in Big Data   Order a copy of this article
    by Maithri C, CHANDRAMOULI H 
    Abstract: MapReduce is regarded as one of the major enabling methods in the big data community for handling the ever-increasing demands on computer resources imposed by enormous datasets. Currently, Googles MapReduce or its open-source equivalent Hadoop are well-known solutions for these types of applications. Several techniques are developed based on Hadoop such as apriori(M), HFUPM, apriori, EFUPM. The conventional algorithms of clustering schemes suffer from many challenging issues as clustering error, and computational complexity. To overcome the challenges of existing techniques we develop a novel approach to data pre-processing to extract the significant information. After pre-processing the data is processed applying neuro fuzzy-based scheduler to obtain semantic relationship between user queries. The fuzzy logic considers query time, query length, query expiry, total queries, CPU usages, and task activity. The proposed approach is implemented using a Hadoop platform. The comparative study shows a significant improvement in MapReduce performance for huge dataset.
    Keywords: distributed systems; data mining; clustering; Hadoop; MapReduce; big data; fuzzy-logic; machine learning; HDFS; computing performance.
    DOI: 10.1504/IJBDM.2023.10050228