title: A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.
abstract: Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA.
authors: Catherine
authors: C
authors: Jayapandian
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA ; Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
authors: Annan
authors: A
authors: Wei
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA ; Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
authors: Priya
authors: P
authors: Ramesh
authors: Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA.
authors: Bilal
authors: B
authors: Zonjy
authors: Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA.
authors: Samden D
authors: SD
authors: Lhatoo
authors: Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
authors: Kenneth
authors: K
authors: Loparo
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA ; Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
authors: Guo-Qiang
authors: GQ
authors: Zhang
authors: Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA ; Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
authors: Satya S
authors: SS
authors: Sahoo
keywords: MapReduce; cloudwave signal format; electrophysiological signal data; epilepsy and seizure ontology; epilepsy research