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Image and Video Analysis (IVA) has been ongoing for several decades and has come up with impressive techniques for object identification, re-identification, activity detection etc. A large number of techniques have been developed and used for processing video frames to detect objects and situations from videos. Camera angles, lighting effect, color differences, and attire make it difficult to analyze videos. Several approaches for searching, and querying videos and images have been developed using indexing and other techniques. This thesis takes a novel approach by converting a video (through extraction of its contents) into a representation over which queries can be specified. This is similar to querying a relational database but on contents extracted from a video and represented using a richer data model. This thesis focuses on new operators needed and their relevance to express real-world situations, such as Alert if the same person came through the same door n times within an hour. model that is needed for representing extracted video contents. The long-term goal is to significantly augment the image and video analysis capabilities for querying.