Enabling real-time information service on telehealth system over cloud-based big data platform

Abstract

A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems don't consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients' information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.

The Internet of Things, dynamic data and information processing capabilities, and operational agility

“…The IoTs does not only enable the collection of valuable data but also assists in producing real-time information useful for operational agility. These IoTsenabled capabilities are vital to building DDIPCs (Wang et al, 2017;Xu et al, 2016).…”

“…This study contributes to the existing debate on the usefulness of the IoTs in four important ways. First, it contributes theoretically by advancing arguments based on traditional dynamic capability perspectives (e.g., Teece, 2007;Teece et al, 1997) and modern dynamic capability requirements (e.g., Mikalef and Pateli, 2017;Wang et al, 2017;Xu et al, 2016;Yang et al, 2013), proposing a framework that utilizes various literature streams such as the IoTs, a dynamic capability-based view and agility to advance the understanding of the important relationships that exist between the use of IoTs, dynamic data and information processing capabilities, and operational agility (Atzori et al, 2010;Xu et al, 2016). So far, the research on IoTs and its impacts had not been established on sound underlying theoretical bases (Atzori et al, 2010;Wang et al, 2017;Xu et al, 2016;Yang et al, 2013), therefore, the contribution of our empirical findings to these aspects substantially closes such research gap.…”

“…Third, the article contributes to the dynamic capabilities literature by proposing and testing the influence of DDIPCs on operational agility. This is an important contribution because, although the existing studies had highlighted the important role played by traditional dynamic capabilities, there was hitherto limited empirical support for how modern data and IT-driven capabilities affect operational agility (Kim et al, 2012;Tan et al, 2015;Wang et al, 2017;Xu et al, 2016).…”

Biomedical Applications Based on Mobile Phone Cell Network Systems

“…Furthermore, as the amount of patients continues to increase, the medical center should use any advanced system efficiently to meet this increasing demand for the system. Increasing requirements for healthcare services have also resulted in the immediate need for efficient and scalable healthcare services [19], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87].…”

Big Data Platform Architecture under The Background of Financial Technology

“…Big Data Mining is based on the platform's big data warehouse and provides data mining and analysis capabilities such as data preprocessing, data exploration, model design, model display and model evaluation. It also provides product selection, product recommendation, product pricing, antifraud and other areas to provide information support [15]. Visual analysis sub-platform integrated use of graphics analysis, cognitive analysis, correlation analysis and other technologies, the big data to multi-dimensional data representation, to provide sales to different dimensions (including space-time dimension) data observation, and then the data more In-depth observation and analysis.…”