On the building of efficient self-adaptable health data science services by using dynamic patterns

Abstract

Health data science systems are becoming key for supporting healthcare decision-making processes. However, these systems should achieve continuous data processing and adapt their behavior to changes arising in real scenarios. Nevertheless, building this type of self-adaptable systems is not trivial, as it requires integrating data analytics and artificial intelligence with cloud computing tools and security and fault tolerance applications. This produces delays in the processing of data, affecting the information delivery in decision-making processes. In this paper, we present the design and implementation of a method to build self-adaptable data science services by using dynamic patterns. This method includes two models: a construction model and a coupling model. The construction model is based on dynamic parallel patterns to create, at design time, in-memory processing structures including as many applications as needed to meet the non-functional requirements (NFRs), such as security and fault tolerance, established by healthcare organizations without affecting the efficiency of a data science system. In turn, the coupling model converts the in-memory processing structures into software blocks that are coupled with the I/O interfaces of data science systems to support the automatic and transparent continuous management of data for facing changes in the incoming workload during execution time. A prototype was implemented to create self-adaptable health data science systems including in-memory processing structures for managing spirometry studies, tomography images, and electrocardiograms. The performance evaluation showed that the dynamic patterns significantly reduced the response time required to prepare contents with NFR characteristics in comparison with solutions from the state-of-the-art such as Nextflow and Makeflow.