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On the Difficulties of Supervised Event Prediction based on Unbalanced Real-World Data in Multi-System Monitoring

dc.contributor.authorSchörgenhumer, Andreas
dc.contributor.authorKahlhofer, Mario
dc.contributor.authorChalupar, Peter
dc.contributor.authorMössenböck, Hanspeter
dc.contributor.authorGrünbacher, Paul
dc.contributor.editorKelter, Udo
dc.date.accessioned2023-02-27T13:59:25Z
dc.date.available2023-02-27T13:59:25Z
dc.date.issued2019
dc.description.abstractOnline failure prediction of performance-critical events is an important task in fault management of software systems. In this paper, we extend our previous multi-system event prediction by analyzing its performance on unbalanced, real-world data, which represents a realistic online scenario. We train a random forest classifier with different data preprocessing configurations, including data augmentation to cope with the extreme class imbalance. The results reveal that the prediction quality of the tested multi-system model drops significantly compared to the balanced scenario. Although our supervised event prediction approach as well as different data preprocessing configurations turned out to be ineffective, we consider the insights of our work valuable for the community.en
dc.identifier.pissn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40484
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 39, Heft 4
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectfailure prediction
dc.subjectfault management
dc.subjectrandom forest classifier
dc.titleOn the Difficulties of Supervised Event Prediction based on Unbalanced Real-World Data in Multi-System Monitoringen
dc.typeText/Conference Paper
gi.citation.endPage40
gi.citation.publisherPlaceBonn
gi.citation.startPage38
gi.conference.date5.-6. November 2019
gi.conference.locationWürzburg
gi.conference.sessiontitle10th Symposium on Software Performance (SSP)

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