An incremental anytime algorithm for mining T-patterns from event streams

Temporal patterns that capture frequent time differences occurring between items in a sequence are gaining increasing attention as a growing research area. Time-interval sequential patterns (also known as T-Patterns) not only capture the order of symbols but also the time delay between symbols, where the time delay is specified as a time-interval between a pair of symbols. Such patterns have been shown to be present in many different types of data (e.g. spike data, smart home activity, DNA sequences, human and animal behaviour analysis and the like) which cannot be captured by other pattern types. Recently, several mining algorithms have been proposed to mine such patterns from either transaction databases or static sequences of time-stamped events. However, they are not capable of online mining from streams of time-stamped events (i.e. event streams). An increasingly common form of data, event streams bring more challenges as they are often unsegmented and with unobtainable total size. In this paper, we propose a mining algorithm that discovers time-interval patterns online, from event streams and demonstrate its capability on a benchmark synthetic dataset.