Provenance-based debugging and drill-down approach for revenue leakage detection and root cause analysis : an application in the telecom domain

Other Title(s)

منهجية التتبع و التنقيب باستخدام مصادر البيانات و أصولها لاكتشاف تسرب الإيرادات و تحليل أسبابه : تطبيق في مجال الاتصالات

Dissertant

al-Abbasi, Wisam

Thesis advisor

Tawil, Adil

University

Birzeit University

Faculty

Faculty of Engineering and Technology

Department

Department of Computer Science

University Country

Palestine (West Bank)

Degree

Master

Degree Date

2018

English Abstract

Revenue Assurance (RA) is considered a top priority function for the telecommunication operators.

Revenue leakage, if not prevented, depending on its severity, could cause a significant revenue loss of an operator.

Detecting and preventing revenue leakage is a key process to assure the efficiency, accuracy and effectiveness of the telecom systems and processes.

There are two general revenue leakage detection approaches: big data analytics and rule-based.

Both approaches seek to detect abnormal usage and profit trend behavior 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 thesis, 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.

The proposed approach has been implemented and tested on thirteen revenue leakage scenarios.

Using defined root-cause gold standard datasets, these scenarios with 26 revenue leakage symptoms have been used to evaluate and validate the proposed approach in terms of completeness and accuracy.

The evaluation results show that the proposed approach can automate the debugging and drill-down of the root causes of these scenarios, and achieves 100% completeness and accuracy for the evaluated root-cause scenarios.

However, its accuracy is directly affected by the accuracy of the contextual information and thus must be accurately represented.

Main Subjects

Information Technology and Computer Science

Topics

• Communication
• Data

No. of Pages

182

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-836451