Multi-Source Learning in a 3G Network

Abstract: By 2020 the world is expected to generate 50 times the amount of data it did in 2011, and much of this increased information will be carried over a mobile network. Understanding the data in the network can assist in mitigating threats to network performance such as congestion and help in network management and the allocation of resources. This master’s thesis aims to investigate to what extent the data carried through the mobile network can be understood in its real-world context, and whether anomalous patterns in the network data profile data can be explained using external data sources. We constructed topic models using LDA for a Twitter stream in London and modeled how the topics’ relative importance changed over time. We examined three anomalous points in the network data profile and studied their correlation with the topic proportions and current weather information. The topic model for Twitter performed poorly due to the difficulty in processing the multifaceted. Twitter corpus. We acknowledge the need to refine the LDA model, to include additional textual data sources, and to understand the different types of anomalous present in the network together with their causes. Such an understanding would allow for a more targeted analysis of anomalous patterns in the network and their relation to the real world.