With the growth in social media, internet of things, and planetary-scale sensing there is an unprecedented need to assimilate spatio-temporally distributed multimedia streams into actionable information. Consequently the concepts like objects, scenes, and events, need to be extended to recognize situations (e.g. epidemics, traffic jams, seasons, flash mobs). This paper motivates and computationally grounds the problem of situation recognition. It describes a systematic approach for combining multimodal real-time big data into actionable situations. Specifically it presents a generic approach for modeling and recognizing situations. A set of generic building blocks and guidelines help the domain experts model their situations of interest. The created models can be tested, refined, and deployed into practice using a developed system (EventShop). Results of applying this approach to create multiple situation-aware applications by combining heterogeneous streams (e.g. Twitter, Google Insights, Satellite imagery, Census) are presented.