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A Cloud Computing Framework for Multimodal Electrophysiological Big Data
出版Case Western Reserve University, 2014
URLhttp://books.google.com.hk/books?id=UvULogEACAAJ&hl=&source=gbs_api
註釋Multimodal electrophysiological data, such as electroencephalography (EEG) and electrocardiography (ECG), are central to effective patient care and clinical research in many disease domains (e.g., epilepsy, sleep medicine, and cardiovascular medicine). Electrophysiological data is an example of clinical 'big data' characterized by volume (in the order of terabytes (TB) of data generated every year), velocity (gigabytes (GB) of data per month per facility) and variety (about 20-200 multimodal parameters per study), referred to as '3Vs of Big Data.' Current approaches for storing and analyzing signal data using desktop machines and conventional file formats are inadequate to meet the challenges in the growing volume of data and the need for supporting multi-center collaborative studies with real-time and interactive access. This dissertation introduces a web-based electrophysiological data management framework called Cloudwave using a highly scalable open-source cloud computing approach and hierarchical data format. Cloudwave has been developed as a part of the National Institute of Neurological Disorders and Strokes (NINDS) funded multi-center project called Prevention and Risk Identification of SUDEP Mortality (PRISM). The key contributions of this dissertation are: 1. An expressive data representation format called Cloudwave Signal Format (CSF) suitable for data-interchange in cloud-based web applications; 2. Cloud based storage of CSF files processed from EDF using Hadoop MapReduce and HDFS; 3. Web interface for visualization of multimodal electrophysiological data in CSF; and 4. Computational processing of ECG signals using Hadoop MapReduce for measuring cardiac functions.Comparative evaluations of Cloudwave with traditional desktop approaches demonstrate one order of magnitude improvement in performance over 77GB of patient data for storage, one order of magnitude improvement to compute cardiac measures for signal-channel ECG data, and 20 times improvement for four-channel ECG data using a 6-node cluster in local cloud. Therefore, our Cloudwave approach helps addressing the challenges in the management, access and utilization of an important type of multimodal big data in biomedicine.