  Given a huge, online stream of time-evolving events with multiple attributes,
such as online shopping logs: (item, price, brand, time), and local mobility
activities: (pick-up and drop-off locations, time), how can we summarize large,
dynamic high-order tensor streams? How can we see any hidden patterns, rules,
and anomalies? Our answer is to focus on two types of patterns, i.e.,
''regimes'' and ''components'', for which we present CubeScope, an efficient
and effective method over high-order tensor streams. Specifically, it
identifies any sudden discontinuity and recognizes distinct dynamical patterns,
''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also
performs multi-way summarization for all attributes (e.g., item, price, brand,
and time) and discovers hidden ''components'' representing latent groups (e.g.,
item/brand groups) and their relationship. Thanks to its concise but effective
summarization, CubeScope can also detect the sudden appearance of anomalies and
identify the types of anomalies that occur in practice. Our proposed method has
the following properties: (a) Effective: it captures dynamical multi-aspect
patterns, i.e., regimes and components, and statistically summarizes all the
events; (b) General: it is practical for successful application to data
compression, pattern discovery, and anomaly detection on various types of
tensor streams; (c) Scalable: our algorithm does not depend on the length of
the data stream and its dimensionality. Extensive experiments on real datasets
demonstrate that CubeScope finds meaningful patterns and anomalies correctly,
and consistently outperforms the state-of-the-art methods as regards accuracy
and execution speed.
