Concurrent Healthcare Data Processing and Storage Framework Using Deep-Learning in Distributed Cloud Computing Environment
| Autor: | Shengguang Yan, Jaebok Seo, Minmin Lin, Lijuan He |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Distributed computing Concurrency Reliability (computer networking) 020208 electrical & electronic engineering Search engine indexing Information processing Process (computing) Cloud computing 02 engineering and technology Service provider Computer Science Applications Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Concurrent computing Electrical and Electronic Engineering business Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 17:2794-2801 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2020.3006616 |
| Popis: | Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this article, retrieval and storage-based indexing framework (RSIF) is designed to improve the concurrency of user and service provider access to the cloud-stored healthcare data. Concurrency is achieved through replication-free and continuous indexing and time-constrained retrieval of stored information. The process of classifying the constraints for data augmentation and update is performed using deep learning for all the storage instances. Through conditional assessment, the learning process determines the approximation of indexing and ordering for storing and retrieval, respectively. This helps to reduce the time for access and retrieval concurrently, provided the process is not dependent. The simulation analysis using the metrics discontinuous indexing, replicated data, retrieval time, and cost proves the reliability of the proposed framework. |
| Databáze: | OpenAIRE |
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