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There is a growing emphasis to find alternative non‐traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In‐home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real‐time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real‐time patient monitoring, and automated decision support (semantic reasoning) based on well‐defined QoS constraints. In this study, the authors review the state‐of‐the‐art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.
Shah et al. (Sat,) studied this question.