A stream query language TPQL for anomaly detection in facility management

In facility management for plants and buildings, needs of facility diagnosis for saving energy or facility management cost by analyzing time series data from sensors of equipments in facilities have been increasing. This paper proposes a relation-based stream query language TPQL (Trend Pattern Query Language) for expressing constraints in time series data for anomalies detection in facilities. The features of TPQL are the following. (1) TPQL introduces a convolution operator into a stream query language in order to describe constraints over sliding window. A convolution operator which takes a window function as an argument can express various domain dependent functions extracting feature over sliding windows such as duration constraint and hunting constraint. (2) TPQL introduces time-interval based join into stream query language in order to join time series data with different sampling rates.