Media analysis and mining involve processing of large quantities of real-time and/or stored data and measurements. Data (whether captured in real-time through sensory measurements or processed, materialized, and stored for later use) are many times accurate only within a margin of error. Moreover, in many applications, the utility of a data element to a particular analysis task depends on the usage context. The fundamental principles that govern the next generation of media/data analysis middleware must include data and operator imprecision, relevance of data to a particular analysis task, and the interest and expertise of the knowledge consumer. In this talk, I will discuss challenges for and opportunities in developing efficient and effective analysis middleware to support large scale data processing and decision making applications, where the data elements, metadata, and the operations on the data may be imprecise.