A knowledge extraction framework for call center analytics

Abstract

In the era of Big Data, performing knowledge extraction in large dataset and forming this into readable information such as graphs and story boards are becoming popular for call centers. However, it is not easy to integrate these approaches into the legacies of Computer Telephony Integration (CTI) system which can be used for data analysis. This paper shows a framework that performs knowledge extraction functions to call center data and other data sources such as social networks. The procedures involved are data storing and retrieval, data virtualization and data mining. Before performing knowledge extraction, the needed data are virtualized, and then, the knowledge acquisition module extracts knowledge in form of data patterns where these patterns can be used by call centers for their data reports and analytics. This research paper shows an implementation of components in processing data and knowledge extraction.