Data-compression-based resource management in cloud computing for biology and medicine    (Open Access)

作者：Zhu, Changming^*

来源：Journal of Computing Science and Engineering, 2016, 10(1): 21-31.

DOI：10.5626/JCSE.2016.10.1.21

• Biology and medicine
• Biomedical data
• Biomedical research
• Data reconstruction
• High dimensionality
• Next-generation sequencing
• Radial basis functions
• Resource management

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• 出版日期2016
• 单位上海海事大学