Tutorial CIDM-T Temporal pattern mining in symbolic time point and time interval data

We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. We distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. For each of the main categories we present an algorithm and experimental results in detail. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. The overall goal of the tutorial is to show the audience which temporal data models can be used to represent observations of dynamic processes and how patterns can be mined from them.