We consider the problem of reliability in big data science projects that are comprised of multiple computing platforms and complex architectures that harness data. Specifically on their ability to capture, process and analyze streaming high frequency data from vast complex systems reliably with effective scalability for deployment in vast domains such as clinical care, smart cities or within extreme climatic work environments. This paper introduces a framework to enable reliable data science projects by integrating multiple computing principles of autonomy, local responsibility, fault tolerance, symmetry, decentralization, well-understood building blocks, and simplicity. The designed framework is applied in the development of a decoupled data pipeline demonstrated through a case study on pre-deployment acclimation strategies that is continuously monitored to ensure reliability and availability is effectively quantified.

The potential for recommendation systems integrated within clinical workflows for effective dissemination of vital information needed in decision making at the bedside is explored in this paper. Our premise is that by utilizing big data analytics platforms for processing high frequency physiological data from multiple patients, fused with clinical context, we can generate recommendations on patients detected as potential for onset of conditions, and that if such information is communicated on time to the appropriate health care providers could have an impact when making decisions on care of critically ill patients. To support this, we have designed and developed an alert notification subsystem that combines vast analytics to detect abnormal patient's physiology, determine who is on service at the bedside and then generate appropriate notification to that care provider during their schedule time in a hospital critical care unit.

Data platforms with an ability to capture, process and analyze high frequency streaming data from vast complex systems reliably with high scalability is the central problem in this paper. Particularly when developed data platforms are to be deployed within extreme climatic conditions. We have developed a decoupled data pipeline by fusing multiple services within edge and cloud computing paradigms following design principles in software reliability to provide optimal services during vast human studies in extreme climatic simulations. The data pipeline is demonstrated through a pre-deployment acclimation case study in climatic chambers. Performance evaluation of each service within the developed pipeline shows high levels of reliability and availability across multiple testing and study simulations. As a result, real-time data from multiple complex data sources are made available for on time personalized analytics for analysis of physiological responses to austere environmental conditions.

The growth of buildings embedded with technologies that can monitor the internal building environment with respect to energy consumption such as heating, ventilation, air conditioning, wind, motion as well occupancy have an immense potential. From energy management, occupancy administration, security maintenance as well as improving the health and quality of life for humans in indoor or outdoor spaces. These potentials can be realized by a clear understanding of the interplay between vast environmental conditions, humans and their health as well as the many smart products they interact with in their lives. This is a complex process that requires thorough testing and evaluation within smart buildings simulation environments where multiple buildings data can be generated and then effectively analyzed. This can be facilitated by a robust data management process that utilizes big data computing technologies to harness large volumes, variety and velocity of data that can be captured within smart buildings while maintain the security and privacy of data sources.In this paper we describe a smart building architecture that has been designed and developed for management of data from a smart building. In particular the architecture enables acquisition, processing and distribution of simulated environmental building data to multiple consumers and workflows for further processing and analysis locally and in a high performance cloud computing platform. The research premise is that such an architecture enables effective management of multiple data sources within climatic based simulated testing in smart buildings to further research.