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    Leveraging Data Analytics Across Digital Product Development Stages: A Systematic Review and Conceptual Framework
    (2026-06-01)
    Alamsyah, Noor
    ;
    Thanasopon, Bundit
    ;
    Jamsri, Pornsuree
    Background: Data analytics (DA) is a field that has expanded greatly and is an important tool for digital product development, and has captured researcher and practitioner interest. Nevertheless, from an Information Systems and Business Intelligence (IS/BI) view, there is still a lack of knowledge regarding the role that data analytics plays in the digital product development lifecycle for decision making. The volume and the complexity of digital product innovation and analytics continues to increase, thereby further enhancing the need for existing knowledge to be consolidated in this area. Objective: This research systematically reviewed the latest academic research in the field of data analytics in digital product development and explain the specific uses of data analytics in the different stages of digital product development for supporting decision making and innovation activities. Methods: This study followed the systematic literature review method through ScienceDirect, IEEE Xplore and Emerald databases. Upon initial search, 1,554 articles were found; 33 relevant articles were identified after a structured screening and eligibility assessment of the articles in line with Kitchenham's protocol. Results: The results reveal the differentiated use of data analytics in the various stages, namely opportunity identification through text mining, feasibility assessment through predictive modelling, prototyping through digital twins and generative design and market responsiveness through predictive analytics and recommender systems. Even with analytical processes and concepts in place, companies often face challenges due to data integration issues, analytical skill, and organizational preparedness. The data quality issues, algorithmic bias, ethical considerations, or lack of algorithm transparency all contribute to these limitations, hindering the full potential of data analytics in digital product development. Conclusion: To realize more effective and aligned outcomes of innovation, it is important to understand how data analytics can assist in decision making throughout the digital product development lifecycle. This research is valuable for researchers and practitioners as it provides a structured conceptual framework for association of analytics initiatives with digital product development goals. There is potential for this work to be extended in future studies, involving the creation and validation of scalable analytics frameworks in various organisational contexts.
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    A Scalable Service Architecture with Request Queuing for Resource-Intensive Tasks
    (2020-06-01)
    Chaowvasin, Kasidis
    ;
    Sutanchaiyanonta, Pun
    ;
    Kanungsukkasem, Nont
    ;
    Leelanupab, Teerapong
    Deploying Machine Learning (ML) prediction or Data Analytic (DA) process as a service in a Web API is not a trivial task. A number of settings and dependency requirements must be met to provide ML or DA successful solutions. In addition, an application that utilizes such an API needs to be always available to serve multiple users who can concurrently submit their requests. ML modeling or DA processing is a resource-intensive task, which can take a massive amount of time to process. Some tasks may take just a few minutes or hours while others may take several days to complete. In this paper, we design and develop a scalable architecture of API services for hosting ML models or DA functionalities in a production-grade deployment. The technologies of containerization and container orchestration, i.e., Docker and Kubernetes, have been employed to automate the deployment, scaling, and management of containerized ML or DA instances. To meet high-scale and high-availability requirements, the open-source message broker, i.e., RabbitMQ, is also used and containerized in Docker for scheduling multiple requests as task messages. These messages are then put into a task queue so that they will be processed later consecutively. Also, Nginx and Node.js with Express.js have been used and containerized as a web server and an API provider, respectively. We use a case-study of an intelligent system for processing documents about national research granting to validate our architecture.
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    Dependable Sensing System for Pig Farming
    (2019-11-01)
    Ariyadech, Sripong
    ;
    Bonde, Amelie
    ;
    Sangpetch, Orathai
    ;
    Woramontri, Woranun
    ;
    Siripaktanakon, Wachirawich
    We have deployed our smart sensing system in a real commercial farm complex for at least 17 months. One of critical factors to success of the sensing system deployment is the resilience and fault tolerance of the system in a harsh environment with unreliable infrastructure and limited access. Power interruptions and intermittent connectivity is not uncommon. Sensors must work even submerging in animal excretion. Correct and continuous streams of sensor data is essential to our smart farming analytics. To make our sensing system sustain such challenges, we have designed and implemented the system with a capability of self-rejuvenation to ensure system liveness. We also equip it with our anomaly detection system to examine small sensor connectivity logs in order to identify potential faulty or deteriorating sensors or external event abnormality with minimal manual intervention.