The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an effective and elegant way. In this paper we present an event cube (EC) model which is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. Specifically, based on the essential event elements of 5W1H (i.e., When, Where, Who, What, Why, and How), the EC model is developed to organize the discovered events from multiple dimensions, to operate on the events at various levels of granularity, so as to facilitate analyzing and mining hidden/inherent relationships among the events effectively. Case studies are provided to illustrate the usages and show the benefits of EC facilities in on-line analytical processing of events and their relationships.