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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].
Due to technical limitations and medical constraints, information systems able to manage such data flow are difficult to deploy.
We extend the spatio-temporal data mining framework that we have developed earlier to analyze and manage such data [ 5].
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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.
Recent advances in multimedia technology have led to tremendous increases in the available volume of video data, thereby creating a major requirement for efficient systems to manage such huge data volumes.
However, the massive proliferation and extensive use of media data arising from its easy-to-copy nature also pose new challenges to effectively manage such abundance of data (e.g., fast media searching, indexing) and protection of intellectual property of multimedia data.
There is a need to manage such large volumes of data and to cluster them easily for data analytics while minimizing maximum inter-cluster distances and managing large datasets.
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