Mining State Dependencies Between Multiple Sensor Data Sources

Pattern mining over data streams is critical to a variety of applications such as prediction and evolution of weather phenomena or anomaly detection in security applications. Most of the current techniques attempt to discover associations between events appearing on the same data stream but are not able to discover associations over multiple heterogeneous data streams. In this work, we aim to identify temporal dependencies between data streams. We represent event streams by state streams that are induced by the streams’ events themselves. Each state has a duration, represented as a set of disjoint time intervals with respect to the events that occurred in the stream. Temporal relations between these interval sets infers dependencies between the corresponding datasources. Our interval-based approach is robust to the temporal variability of events that characterizes the time intervals during which the events are related. It links two types of events if the occurrence of one is often followed by the appearance of the other in a certain time interval. The proposed approach determines the most appropriate time intervals of a temporal dependency whose validity is assessed by a χ test. As several intervals may redundantly describe the same dependency, the approach retrieves only the few most specific intervals with respect to a dominance relationship over temporal dependencies, and thus avoids the classical problem of pattern flooding in data mining. TEDDY algorithm, TEmporal Dependency DiscoverY, prunes the search space while certifying the discovery of all valid and significant temporal dependencies. We present empirical results on simulated data to show the scalability and the robustness