17,366 research outputs found
Architecture for Analysis of Streaming Data
While several attempts have been made to construct a scalable and flexible
architecture for analysis of streaming data, no general model to tackle this
task exists. Thus, our goal is to build a scalable and maintainable
architecture for performing analytics on streaming data.
To reach this goal, we introduce a 7-layered architecture consisting of
microservices and publish-subscribe software. Our study shows that this
architecture yields a good balance between scalability and maintainability due
to high cohesion and low coupling of the solution, as well as asynchronous
communication between the layers.
This architecture can help practitioners to improve their analytic solutions.
It is also of interest to academics, as it is a building block for a general
architecture for processing streaming data
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
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