Given a stream $S = (s_1, s_2, ..., s_N)$, a $φ$-heavy hitter is an item $s_i$ that occurs at least $φN$ times in $S$. The problem of finding heavy-hitters has been extensively studied in the database literature. In this paper, we study a related problem. We say that there is a $φ$-event at time $t$ if $s_t$ occurs exactly $φN$ times in $(s_1, s_2, ..., s_t)$. Thus, for each $φ$-heavy hitter there is a single $φ$-event which occurs when its count reaches the reporting threshold $φN$. We define the online event-detection problem (OEDP) as: given $φ$ and a stream $S$, report all $φ$-events as soon as they occur. Many real-world monitoring systems demand event detection where all events must be reported (no false negatives), in a timely manner, with no non-events reported (no false positives), and a low reporting threshold. As a result, the OEDP requires a large amount of space (Omega(N) words) and is not solvable in the streaming model or via standard sampling-based approaches. Since OEDP requires large space, we focus on cache-efficient algorithms in the external-memory model. We provide algorithms for the OEDP that are within a log factor of optimal. Our algorithms are tunable: its parameters can be set to allow for a bounded false-positives and a bounded delay in reporting. None of our relaxations allow false negatives since reporting all events is a strict requirement of our applications. Finally, we show improved results when the count of items in the input stream follows a power-law distribution.