Software Quality Injection (QI): A Quality Driven Holistic Approach for Optimising Big Healthcare Data Processing
Year: 2025
Type: book-chapter
Abstract: The rapid growth of big data is driving innovation in software development, with advanced analytics offering transformative opportunities in applied computing. Big Healthcare Data (BHD), characterised by multi-structured and complex data types, requires resilient and scalable architectures to effectively address critical data quality issues. This paper proposes a holistic framework for adopting advanced cloud-computing strategies to manage and optimise the unique characteristics of BHD processing. It outlines a comprehensive approach for ensuring optimal data handling for critical healthcare workflows by enhancing the system's quality attributes. The proposed framework prioritises and dynamically adjusts software functionalities in real-time, harnessing sophisticated orchestration capabilities to manage complex, multi-dimensional healthcare datasets, streamline operations, and bolster system resilience.
DOI: 10.3233/shti250065
Authors: Tia Haddad, Pushpa Kumarapeli, Simon de Lusignan, Souheil Khaddaj, Sarah Barman
Institutions: Kingston University London, University of Oxford
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409244829