KEYWORDS: Clouds, Data archive systems, Data storage, Calibration, Data modeling, Computing systems, Linear regression, Data processing, Interferometry
The amount of astronomical data that needs to be archived, calibrated, and processed continues to increase as telescopes and observing instruments advance. Securing necessary resources to store and process ever-increasing data is an operational challenge. To solve these issues, we conducted a demonstration experiment using ALMA archived data to efficiently utilize a commercial cloud for archive storage and data analysis pipeline processing. In archiving, a hybrid configuration combining on-premise storage and cloud based short-term and long-term storages is cost-effective, considering the trends on the number of data downloads over time since the data was obtained. In the data analysis processing, information on processing time and resource usage, such as memory and CPU core, measured during the pipeline process of approximately 400 observation data sets was analyzed, and a model was created to estimate processing time and the required amount of resources from the observation parameters. Based on the model created, the amount of required resources is predicted based on observation parameters, and an instance with the necessary and sufficient resources for pipeline processing is launched on demand on the cloud. These pipeline processes were completed with resources in a processing time comparable to that of on-premise ones. Since prices, services, computing resources, etc. on commercial cloud are updated frequently, we plan to continue making periodic estimates.
Deep Learning-based medical imaging research has been actively conducted thanks to its high diagnostic accuracy comparable to that of expert physicians. However, to apply developed Computer Aided Diagnosis (CAD) systems to various data collected from different hospitals, we should prepare sufficient training data in terms of quality/quantity; unfortunately, especially in Japan, we need to overcome each hospital’s different ethical codes to obtain such multi-institutional data. Therefore, we built a cloud platform for (i) collecting multi-modal large- scale medical images from hospitals through medical societies and (ii) conducting various Deep Learning-based CAD research via collaboration between Japanese medical societies and institutes of informatics. Each hospital first provides the data to the corresponding medical society among 6 societies (e.g., Japan Radiological Society and Japanese Society of Pathology) based on their modality among 8 modalities (e.g., Computed Tomography and Whole Slide Imaging (WSI)); then, each society uploads them, possibly with annotation, to our cloud plat- form established in November 2017. We have collected over 80 million medical images by December 2019, and over 60 registered researchers have conducted CAD research on the platform. We presented the achieved results at major international conferences/in medical journals; their ongoing clinical applications include remote WSI diagnosis. We plan to further increase the number of images/modalities and apply our research results to a clinical environment.
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