Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. In this paper, we propose an idea for monitoring and grouping the events that occur in tweet streams. This may help us to scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. We capture the events using four operations (create, absorb, split and merge). The posted tweet is grouped by a keyword in that post. When a tweet is posted it is compared by another post and forms a group using the words in the tweet. In addition, we also find the nearest neighbour who lies in the similar line based on the tweets posted by them. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List (MIL), to manage dynamic event databases for the acceleration of large-scale event search and update. Extensive experiments have been conducted on a large-scale real-life tweet dataset. The results demonstrate the promising performance of our event indexing and monitoring methods on both efficiency and effectiveness.