Tia Haddad, Pushpa Kumarapeli, Simon de Lusignan, Souheil Khaddaj, Sarah Barman
{"title":"Software Quality Injection (QI): A Quality Driven Holistic Approach for Optimising Big Healthcare Data Processing.","authors":"Tia Haddad, Pushpa Kumarapeli, Simon de Lusignan, Souheil Khaddaj, Sarah Barman","doi":"10.3233/SHTI250065","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"323 ","pages":"141-145"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in health technology and informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/SHTI250065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.
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