META: Multi-resolution Framework for Event Summarization

META: Multi-resolution Framework for Event Summarization Report Title Event summarization is an effective process that mines and organizes event patterns to represent the original events. It allows the analysts to quickly gain the general idea of the events. In recent years, several event summarization algorithms have been proposed, but they all focus on how to find out the optimal summarization results, and are designed for one-time analysis. As event summarization is a comprehensive analysis work, merely handling this problem with a single optimal algorithm is not enough. In the absence of an integrated summarization solution, we propose an extensible framework – META – to enable analysts to easily and selectively extract and summarize events from different views with different resolutions. In this framework, we store the original events in a carefully-designed data structure that enables an efficient storage and multiresolution analysis. On top of the data model, we define a summarization language that includes a set of atomic operators to manipulate the meta-data. Furthermore, we present 5 commonly used summarization tasks, and show that all these tasks can be easily expressed by the language. Experimental evaluation on both real and synthetic datasets demonstrates the efficiency and effectiveness of our framework. Read More: http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.70 Conference Name: Proceedings of the 2014 SIAM International Conference on Data Mining Conference Date: May 01, 2014 META: Multi-resolution Framework for Event Summarization Yexi Jiang, Chang-Shing Perng, Tao Li Florida International University IBM T.J Watson Research Center