Data mining has been applied to knowledge discovery in various non-traditional application domains, each with its own differentiating characteristics that make the use of only traditional data mining algorithms untenable. In this work we carefully motivate a new domain for data mining, which involves discovering the navigational patterns of users in environments with dynamically changing content (for example, cable-TV viewers). The access sites we study display continuously changing content unlike web pages that are relatively static, thus, calling for new algorithms for identifying interesting navigational patterns. This paper proposes an algorithm for discovering user navigational behaviour in response to streaming content, based on behavioral predicates. A second algorithm is proposed for discovering navigational paths frequently traversed by the system's users. The algorithms incorporate the rich temporal semantics existing in sites with streaming content.