Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8493
Title: Provenance-based root cause analysis for revenue leakage detection: a telecommunication case study
Authors: Abbasi, Wisam 
Taweel, Adel 
Keywords: Root cause;Machine learning - Computer simulation;Debugging in computer science - Computer programs;Provenance;Risk (Insurance)
Issue Date: 2018
Publisher: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract: Revenue Assurance (RA) represents a top priority function for most of the telecommunication operators worldwide. Revenue leak- age, if not prevented, depending on the severity of the leakage a ecting their pro tability and continuity, could cause a signi cant revenue loss of an operator. Detecting and preventing revenue leakage is a key process to assure telecom systems and processes e ciency, accuracy and e ectiveness. There are two general revenue leakage detection approaches: big data analytics and rule-based. Both approaches seek to detect abnormal usage and pro t trend behaviour and revenue leakage based on certain patterns or prede ned 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 de- ploys a provenance-based model is proposed. The model represents the work ow 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.
URI: http://hdl.handle.net/20.500.11889/8493
DOI: 10.1007/978-3-319-98379-0_20
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