Automatic Mining of Large IoT Sensor Tensor
Takato Honda, Yasuko Matsubara, Yasushi Sakurai · 2018
Given a large collection of multiple time-evolving sensor sequences, how can we capture the transitions of time series patterns? How can we find individual differences between different sequences? In this paper we present CUBEMARKER, an effective method for capturing multi-aspect features of sensor sequences, which provides a compact and powerful representation of sensor behavior. Our second contribution is a novel, scalable, and parameter-free algorithm. CUBEMARKER performs two-way mining for all attributes. Specifically it discovers multi-aspect time series patterns (human motion, smart factory, etc) and groups of patterns simultaneously. Extensive experiments on real datasets show that CUBEMARKER is effective in that it can capture meaningful patterns for various real sensor datasets.