In today’s competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.
CITATION STYLE
Mohan, R., Chaudhury, S., & Lall, B. (2017). Incremental Learning of Non-stationary Temporal Causal Networks for Telecommunication Domain. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10597 LNCS, pp. 501–508). Springer Verlag. https://doi.org/10.1007/978-3-319-69900-4_64
Mendeley helps you to discover research relevant for your work.