Recent developments of modern technologies such as cloud computing, wearable sensor devices and big data have significantly impacted people's daily lives, and offer real potential for an Internet-wide, people-centric ecosystem. These advances in technology will considerably extend human capabilities in acquiring, consuming and sharing personal health information. A future in which we are all equipped with devices and sensors that passively collect data and interpret our health and activity status is not far away. The key challenge that we will be facing is how to effectively manage and use that big data. In this paper, we propose a cloud-based platform for health sensor data management, named Wiki-Health, which will provide a potential solution for storing, tagging, retrieving, analysing, comparing and searching health sensor data.
Concinnity takes sensor data from collection to final product via a cloudbased data repository and easy-to-use workflow system. ensors are now the dominant source of data generated worldwide, producing 1,250 billion gigabytes of data in 2010. 1 Sensor data is also high velocity (collected and processed in real time) and highly variable (collected by diverse sensor networks). Because these sensor data are interconnected, the volume of the integrated data is even larger. A sensor data platform should thus be able to support both a high volume of data and largescale applications. However, the bottleneck is not how to collect, store, and manage the resulting big sensor data, given that various technologies exist to address these elements, but how to build systems that enable us to use such data effectively.We propose a platform, Concinnity, that enables the collaborative contribution, sharing, and use of big sensor data. Concinnity takes sensor data from collection to fi nal product via a cloud-based data repository and easy-to-use workfl ow system. It supports rapid development of applications built on sen-sor data using data fusion and the integration and composition of models to form novel workfl ows. These key features enable value to be derived from sensor data effi ciently.
Challenges in Sensor DataAlthough sensor data is certainly valuable to its owners for their specifi c purposes, it could also be valuable to a wider audience. Making this data widely available, however, requires revisiting key challenges in sensor system design. (We should note that in this article we focus exclusively on the informatics challenges of big sensor data, upstream of the lower-level hardware or networking considerations.)
Crowdsourcing and CollaborationThe fi rst challenge in designing systems for collaborative use of large-scale sensor data relates to creating an ecosystem in which users get mutual benefi t from contributing, sharing, and using data.
In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant’s demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.
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