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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Curbera, F., Doganata, Y., Martens, A., Mukhi, N.K., Slominski, A.: Business provenance – a technology to increase traceability of end-to-end operations. In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5331, pp. 100–119. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88871-0_10
Greenwood, M., et al.: Provenance of e-science experiments-experience from bioinformatics, pp. 223–226 (2003)
Revenue assurance how to stop bleeding and start leading. https://clarity.sutherlandglobal.com/blog/accounting-minute/revenue-assurance-how-to-stop-bleeding-and-start-leading/. Accessed 7 Jan 2018
Global Telecom Revenue Assurance Survey 2013. http://www.ey.com/Publication/vwLUAssets/Global_telecoms_revenue_assurance_survey_2013/$FILE/Global_revenue_assurance_survey_2013.pdf. Accessed 7 Jan 2018
Imran, M., Hlavacs, H.: Provenance in the cloud: why and how? In: The Third International Conference on Cloud Computing, GRIDs, and Virtualization, pp. 106–112. Cloud Computing (2012)
Moreau, L.: The provenance of electronic data. Commun. ACM 51(4), 52–58 (2008)
Provenance. https://en.wikipedia.org/wiki/Provenance. Accessed 7 Jan 2018
Revenue Assurance. https://en.wikipedia.org/wiki/Revenue_assurance. Accessed 7 Jan 2018
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Abbasi, W., Taweel, A. (2018). Provenance-Based Root Cause Analysis for Revenue Leakage Detection: A Telecommunication Case Study. In: Belhajjame, K., Gehani, A., Alper, P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science(), vol 11017. Springer, Cham. https://doi.org/10.1007/978-3-319-98379-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-98379-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98378-3
Online ISBN: 978-3-319-98379-0
eBook Packages: Computer ScienceComputer Science (R0)