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Detecting trend deviations with generic stream processing patterns
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DOI：10.1016/j.is.2019.101446.png)
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Information systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow parameters are defined. This process has been implemented and tested in a real-world event stream processing tool, called BeepBeep. Experimental results show that deviations from a reference trend can be detected in real-time for streams producing up to thousands of events per second. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
COMPLIANCE CHECKING
ANOMALY DETECTION
OUTLIER
WORKFLOW
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