Enterprise Information System Structure Partition Algorithm Based on Link Feature

In the diversified market competition environment promoted by emerging information technology, diversified business functions run collaboratively based on the shared data, that is, it has become the norm for enterprise data to be shared by various business functions. Therefore, there is a need for the partition of enterprise information system structure based on business functions and data usage connections to improve the collaboration efficiency of various business functions based on shared data. This research proposes a system structure partition algorithm based on link feature (SPLF). The SPLF algorithm takes non-interference sequence as the theoretical basis, and introduces link feature and link shared degree of sets as a measure of similarity between entities or sets. The SPLF algorithm includes two stages: entity link feature calculation and pre-clustering stage, and two-dimensional entity set clustering stage. Experiment shows that the clustering results produced by the SPLF algorithm have high cohesion and accuracy, and can effectively aggregate discrete original data from two dimensions in parallel, and realize sub-system partition according to the clustering results. The SPLF algorithm adopts the idea of joint clustering, and takes the sharing of links as the basis for similarity measurement, and extends the traditional clustering method from one-dimensional to two-dimensional. Based on the SPLF algorithm, this research proposes an enterprise information system structure partition framework, which structurally solves the problem of enterprise business function and data organization under the background of big data, and provides an effective solution for data sharing and cooperation for various business functions of the enterprise, so as to improve business processing efficiency and competitiveness.