数字图书馆知识发现的数据驱动机制及绩效优化研究

Research on Data Driven Mechanism and Performance Optimization of Knowledge Discovery in Digital Library

[目的/意义]数据驱动环境下,探讨数字图书馆知识发现平台的数据驱动机制和优化方案有利于从方法论认识层面为其供给侧改革提供理论支持。[方法/过程]借助系统动力学方法,通过仿真呈现数字图书馆知识发现的数据驱动的动力形成机制;从绩效优化视角,运用粒计算方法为其驱动优化提供可行方案。[结果/结论]影响数字图书馆知识发现的数据驱动因素主要包括数据维度、语义关联维度、可视化维度和价值维度,从维度的形成和绩效作用关系看,数字图书馆知识发现的数据驱动是一个螺旋式发展的动态系统,其绩效优化的关键点就在于数据的知识价值开发程度,经实证研究,将知识粒度作为实现其优化的切入点能较好地提升数字图书馆知识发现的数据驱动效果。

[Purpose/significance]Under the data-driven environment,exploring the data-driven mechanism and optimization scheme of knowledge discover platform of digital library is conducive to provide theoretical support for supply -side reform from the perspective of methodology.[Method/process]By means of the system dynamics method,the data -driven dynamic formation mechanism of digital library knowledge discovery is presented through simulation.From the perspeetive of performance optimization,the granular computing method is used to provide a feasible solution for its drive optimization.[Result/conclusion]The data driving factors that influence the knowledge discovery of digital library mainly include data dimension,semantic association dimension,visualization dimension and value dimension.From the perspective of the formation of dimensions and the role of performance,the data drive of digital library knowledge discovery is a dynamic system of spiral development,the key point of performance optimization lies in the exploitation degree of knowledge value of data.The knowledge granularity as the starting point to achieve its optimization can better improve the data -driven effect of digital library knowledge discovery,according to the experimental studies.