A Time Series Clique (TSC) consists of multiple time series which are related to each other by natural relations. The natural relations that are found between the time series depend on the application domains. For example, a TSC can consist of time series which are trajectories in video that have spatial relations. In conventional time series retrieval, such natural relations between the time series are not considered. In this paper, we formalize the problem of similarity search over a TSC database. We develop a novel framework for efficient similarity search on TSC data. The framework addresses the following issues. First, it provides a compact representation for TSC data. Second, it uses a multidimensional relation vector to capture the natural relations between the multiple time series in a TSC. Lastly, the framework defines a novel similarity measure that uses the compact representation and the relation vector. We conduct an extensive performance study, using both real-life and synthetic data sets. From the performance study, we show that our proposed framework is both effective and efficient for TSC retrieval.
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