Automatic Resolver Group Assignment of IT Service Desk Outsourcing in Banking Business

Abstract

This paper proposes a framework of automatic resolver group assignments of IT service desk outsourcing in banking firms. Recently, service desk technologies have not addressed the problem of performance in resolving incidents dropped due to overwhelming reassignments. This article makes two contributions: (1) data preparation procedures proposed for text mining discovery algorithms; and (2) rule generation from data based on decision tree procedures proposed for knowledge acquisition. In the experiments, we acquired the incident dataset from Tivoli CTI system as text documents and then conducted data pre-processing, data transforming, decision-tree-from-text mining, and decision-tree-to-rules generation. The method of model was validated using the test dataset by the 10-fold cross validation technique. The experimental results indicated that ID3 method could correctly assign jobs to the right group based on the incidents documents in text or typing keywords. Furthermore, the rules resulting from the rule generation from the decision tree could be properly kept in a knowledge database in order to support and assist with future assignments.