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Thereby, the development of image and video content-based retrieval systems (capable of efficiently managing such data) has increased as well the academic interest in the area [1, 2].
The core i2b2 fact table (observation_fact) does not currently have provision for managing such data elements, and therefore, resolving status changes on results or managing updates to clinical observations need to be performed in a 'staging area' outside of the core i2b2 tables, before uploading/updating the data in the repository.
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However, existing systems provide limited capabilities to manage such data.
In this paper, we present a system which can store and manage such data.
However, the required resources to manage such data in a cloud-IoT environment are still a big challenge.
One way to manage such data is cloud storage (and computing) [ 22].
We extend the spatio-temporal data mining framework that we have developed earlier to analyze and manage such data [ 5].
Due to technical limitations and medical constraints, information systems able to manage such data flow are difficult to deploy.
As the volume of folding trajectories produced from high-throughput simulation tools increases drastically, there is an urgent need to compare, analyze, and manage such data.
Creating an effective information system to manage such data requires careful database design, and often necessitates significant adaptations to both design and software as new data items and workflows become apparent once the study has started.
As biological data accumulate at larger scales and increase at exponential paces, thanks principally to higher-throughput and lower-cost DNA sequencing technologies, the number of biological databases that have been developed to manage such data deluge is growing at ever-faster rates.
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