Scalable Spatio-Temporal Top-k Community Interactions Query

The excessive amount of data that online users produce through social media platforms provides valuable insights about users and communities at scale. Existing techniques have not fully exploited such data to help practitioners perform a deep analysis of large online communities. Lack of scalability hinders analyzing communities of large sizes and requires tremendous system resources and unacceptable runtime. This paper introduces a new analytical query that reveals the top-k posts of interest of a given user community over a period of time and in a certain location. We propose a novel indexing framework that captures the interactions of community users to provide a low query latency. Moreover, we propose efficient query algorithms that utilize the index content to prune the search space. The extensive experimental evaluation on real data has shown the superiority of our techniques and their scalability to support large online communities.