META: Multi-resolution Framework for Event Summarization

Abstract

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.

RESTFul

“…Multiple Resolution Techniques. Multiple resolution methods have been successfully used in many tasks in data mining and computer vision [2,6,14,26,45]. Specifically, Buevich et al developed a multi-resolution datastore to pre-processes incoming data on embedded leaf nodes [2].…”

“…Specifically, Buevich et al developed a multi-resolution datastore to pre-processes incoming data on embedded leaf nodes [2]. Jiang et al proposed an integrated event summarization approach to facilitate the multi-resolution analysis of the events [14]. In addition, a framework has been proposed for multi-resolution spatial event forecasting [45].…”

Data-Driven Techniques in Disaster Information Management

“…or in the case that there is a host of members in a neighborhood (e.g., a corporate park), multiple reports about a particular area might be redundant, which calls for a concise, accurate, and consistent summarized view derived from diverse information sources. Data integration and data fusion have been extensively studied in the database community [Haas 2006], and have been applied to analyze multimedia data to further enhance the performance of multimedia content-based retrieval [Bagheri et al 2013], event detection [Corey 2012], event summarization [Jiang et al 2011[Jiang et al , 2014Chakrabarti and Punera 2011], and semantic concept detection [Atrey et al 2010].…”