Multi-Granularity Cognitive Computing—A New Model for Big Data Intelligent Computing

doi:10.11871/jfdc.issn.2096-742X.2019.02.007

Abstract

Abstract:

[Objective] This paper analyzes the research background of big data intelligent computing and associated challenging problems, then introduces multi-granularity cognitive computing, a novel model for big data intelligent computing in the view of cognitive computing. [Methods] Big data intelligent computing is shown to be a way to utilize the value of big data. The data computing mechanism of most traditional big data intelligent computing models is found inconsistent with the cognition mechanism of human brain. This paper introduces the multi-granularity cognitive computing model, which is a model for big data intelligent computing and unifies the “global precedence” law of human brain’s cognition mechanism (from coarse granularity to fine granularity) and the information processing mechanism in computer systems (from fine granularity to coarse granularity). The framework of data-driven granular cognitive computing (DGCC) is introduced. Furthermore, some application examples in intelligent manufacturing process industry are introduced. [Results] It is found that three kinds of scientific problems need to be studied for establishing the data-driven granular cognitive computing model and the integration of knowledge driven and data driven computing mechanisms. Those are multi-granularity space description, multi-granularity joint problem solving and human-computer cognitive mechanism integration. [Conclusion] Through its preliminary exploration on intelligent manufacturing in process industry, it is shown that multi-granularity cognitive computing is an effective new model to solve the problem of data-knowledge fusion in intelligent decision-making based on big data.

Key words: big data intelligence, cognitive computing, granular computing, data mining

Wang Guoyin, Yu Hong. Multi-Granularity Cognitive Computing—A New Model for Big Data Intelligent Computing[J]. Frontiers of Data and Computing, 2019, 1(2): 75-85.

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