Capturing episodes

We are interested in detecting episodes in a data stream that are characterized by a period of time over which a condition holds, usually with a minimum duration. For example, we might want to know whenever any router has a packet-drop rate above 0.3% continuously for more than two minutes. Such episodes can be interesting in their own right for monitoring purposes, but they can also specify target regions for examination over the original or other stream. For instance, for each router-drop episode we detect, we might want to count the number of control messages the router received. We assert the key requirements are to detect the episodes, detect them accurately, and detect them promptly. Current capabilities for data-stream management systems (DSMSs) include functionality, such as pattern-matching and windowed aggregates, that can help with detecting some kinds of episodes. We offer a third alternative, frames, which generalizes the other two. Frames are intervals that segment a data stream into regions of interest. In contrast to windows, frame boundaries can be data dependent, such as when a predicate holds for a given duration, or the maximum and minimum values of an attribute diverge more than a certain amount. We introduce frames and their theory, plus their implementation in the NiagaraST DSMS. We then demonstrate some advantages of frames versus windows, such as better characterization of episodes, on real data sets and explore an extension, fragments, to deal with long episodes.