Applying Social Event Data for the Management of Cellular Networks
This addresses the need for improved Operations, Administration, and Maintenance (OAM) tasks in cellular networks, particularly in urban areas, by leveraging social event data, but it appears incremental as it builds on existing data sources and techniques.
The paper tackles the problem of predicting social events and their impact on cellular network performance by presenting a framework for automatically acquiring and processing social data and associating it with network elements. The system is evaluated in a real deployment, though no concrete numbers are provided in the abstract.
Internet provides a growing variety of social data sources: calendars, event aggregators, social networks, browsers, etc. Also, the mechanisms to gather information from these sources, such as web services, semantic web and big data techniques have become more accessible and efficient. This allows a detailed prediction of the main expected events and their associated crowds. Due to the increasing requirements for service provision, particularly in urban areas, having information on those events would be extremely useful for Operations, Administration and Maintenance (OAM) tasks, since the social events largely affect the cellular network performance. Therefore, this paper presents a framework for the automatic acquisition and processing of social data, as well as their association with network elements (NEs) and their performance. The main functionalities of this system, which have been devised to directly work in real networks, are defined and developed. Different OAM applications of the proposed approach are analyzed and the system is evaluated in a real deployment.