Current systems that publish XML/relational data using XML views are passive in the sense that they can only respond to user-initiated queries over the XML views. Further, existing systems do not support ranked keyword searches over virtual XML views, which is important for exploring and retrieving information from large views. In this dissertation, we propose an XML view system whereby users can place active triggers on virtual (unmaterialized) XML views, and can efficiently evaluate keyword search queries over such views. In this architecture, we present scalable and efficient techniques for processing triggers over nested views by leveraging existing support for SQL triggers over flat relations in commercial relational databases. When evaluating the keyword search queries, our approach exploits indices present on the base data and thereby avoids computing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that it supports top-k results for queries over the virtual view, and the resulting rank order is the same as if the view was materialized. We have implemented our proposed techniques in the context of the Quark XML middleware system. Our performance results indicate that our proposed techniques are a feasible approach to supporting triggers and ranked keyword searches over virtual XML views.