Mining complex temporal dependencies from heterogeneous sensor data streams.

In addition to sensor heterogeneity, monitoring applications must handle different temporal data models (e.g time series, event sequences). In this paper, we address the problem of discovering directly actionable high level knowledge from such data. We model temporal information through interval-based streams describing environment states. We propose an approach to discover efficiently Complex Temporal Dependencies (CTD) between state streams, called CTD-Miner. A CTD is modeled similarly to a conjunctive normal form and describes temporal relations (time delays) between states. CTD-Miner is robust to temporal variability of data and uses a statistical independence test to determine the most appropriate time lags between states. This test is also used to perform pruning on sub-dependencies checking. Finally, we validate our approach via synthetic data and a case study in a real-world smart environment using outdoor cameras and real-time video processing.