Granular computing with multiple granular layers for brain big data processing.
Granular computing with multiple granular layers for brain big data processing.
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DOI：
10.1007/s40708-014-0001-z
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发表时间：
2014-12
影响因子：
--
通讯作者：
Xu J
中科院分区：
文献类型：
--
作者：
Wang G;Xu J
大数据是指数据集的集合，数据集如此庞大和复杂，以至于很难使用现有的理论模型和技术工具进行处理。脑大数据是利用功能磁共振成像、多通道脑电图、脑磁图、正电子发射断层扫描、近红外光谱成像等各种设备采集的最典型、最重要的大数据之一。多颗粒层的颗粒计算，简称多颗粒计算（MGrC），是一种模拟人脑多颗粒智能思维模型的新兴信息处理计算范式。它涉及对称为信息颗粒的复杂信息实体的处理，信息颗粒是在数据抽象和从数据中导出信息甚至知识的过程中产生的。本文分析了MGrC的粒度优化、粒度转换和多粒度联合计算三种基本机制，并探讨了将MGrC引入脑大数据智能处理的潜力。
Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models and technique tools. Brain big data is one of the most typical, important big data collected using powerful equipments of functional magnetic resonance imaging, multichannel electroencephalography, magnetoencephalography, Positron emission tomography, near infrared spectroscopic imaging, as well as other various devices. Granular computing with multiple granular layers, referred to as multi-granular computing (MGrC) for short hereafter, is an emerging computing paradigm of information processing, which simulates the multi-granular intelligent thinking model of human brain. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of information and even knowledge from data. This paper analyzes three basic mechanisms of MGrC, namely granularity optimization, granularity conversion, and multi-granularity joint computation, and discusses the potential of introducing MGrC into intelligent processing of brain big data.