[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 perspective 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.
Li Jie
,
Bi Qiang
,
Xu Pengcheng
,
Mou Dongmei
. Research on Data Driven Mechanism and Performance Optimization of Knowledge Discovery in Digital Library[J]. Library and Information Service, 2019
, 63(3)
: 6
-13
.
DOI: 10.13266/j.issn.0252-3116.2019.03.001
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