Memory management is a critical issue in stream processing involving stateful operators such as join. Traditionally, the memory requirement for a stream join is query-driven: a query has to explicitly define a window for each (potentially unbounded) input. The window essentially bounds the size of the buffer allocated for that stream. However, outputs produced by such approach may not be desirable (if the window size is not part of the intended query semantic) due to the volatile input characteristics. We discover that when streams are ordered or partially ordered, it is possible to use a data-driven memory management scheme for improved performance. In this work, we present a novel data-driven memory management scheme, called Window-Oblivious Join (WO-Join), which adaptively adjusts the state buffer size according to the input characteristics. Our performance study shows that, compared to traditional Window-Join (W-Join), WO-Join is more robust with respect to the dynamic inputs and therefore produces higher quality results with lower memory costs.