Provenance-Based Root Cause Analysis for Revenue Leakage Detection: A Telecommunication Case Study

Abstract

Revenue Assurance (RA) represents a top priority function for most of the telecommunication operators worldwide. Revenue leakage, if not prevented, depending on the severity of the leakage affecting their profitability and continuity, could cause a significant revenue loss of an operator. Detecting and preventing revenue leakage is a key process to assure telecom systems and processes efficiency, accuracy and effectiveness. There are two general revenue leakage detection approaches: big data analytics and rule-based. Both approaches seek to detect abnormal usage and profit trend behaviour and revenue leakage based on certain patterns or predefined rules, however both are mainly human-driven and fail to automatically debug and drill down for root causes of leakage anomalies and issues. In this work, a rule-based RA approach that deploys a provenance-based model is proposed. The model represents the workflow of critical RA functions enriched with contextual and semantic information that may detect critical leakage issues and generate potential leakage alerts. A query model is developed for the provenance model that can be applied over the captured data to automate, facilitate and improve the current process of root cause analysis of revenue leakages.