Sequence Discovery refers to the identification and analysis of patterns or sequences within a set of data. In the context of bioinformatics, researchers aim to discover meaningful patterns in biological sequences such as DNA, RNA, or protein sequences. It involves the use of computational methods and algorithms to identify recurring patterns, motifs, or structures within these sequences.

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Anomalous Event Sequence Detection

Anomalous Event Sequence Detection Anomaly detection has been widely applied in modern data-driven security applications to detect abnormal events/entities that deviate from the majority. However, less work has been done in terms of detecting suspicious event sequences/paths, which are better discriminators than single events/entities for distinguishing normal and abnormal behaviors in complex systems such as cyber-physical systems. A key and challenging step in this endeavor is how to discover those abnormal event sequences from millions of system event records in an efficient and accurate way. To address this issue, we propose NINA, a network diffusion-based algorithm for identifying anomalous event sequences. Experimental results on both static and streaming data show that NINA is efficient (processes about 2 million records per minute) and accurate.