Detecting trend deviations with generic stream processing patterns. Issue 101 (November 2021)

• Record Type: Journal Article
• Authors: Roudjane, Massiva; Rebaïne, Djamal; Khoury, Raphaël; Hallé, Sylvain
• Is Part Of: Information systems. Issue 101(2021)
• Publication Date: 2021-11
• Subjects: Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2019.101446
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: 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 realtime for streams producing up to thousands of events per second. Highlights: We identify scenarios where deviations from a trend need to be detected in logs. We propose a generic pattern for handling the detection of trend deviations. We implement this pattern and test it experimentally to assess its performance.