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An architecture for detecting infrastructure anomalies at Germany’s Federal Employment Agency

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dc.contributor.authorLudsteck, Johannes
dc.contributor.authorSultanow, Eldar
dc.contributor.authorChircu, Alina
dc.contributor.authorHerget, Gebhard
dc.contributor.authorSeßler, Matthias
dc.date.accessioned2021-12-14T10:56:55Z
dc.date.available2021-12-14T10:56:55Z
dc.date.issued2021
dc.description.abstractThe data centers of Germany’s Federal Employment Agency (FEA) provide an information technology (IT) infrastructure that is critical for both external stakeholders and internal processes. For FEA and many other organizations like it, it is essential that any IT infrastructure anomalies - deviations from normal behavior - are detected and their underlying causes are understood and, if appropriate, addressed. In this paper we develop a solution that can help increase the availability of an IT landscape such as FEA’s that is characterized by increasing technical complexity and increasing relevance of its applications. The solution detects IT service anomalies based on IT service access logs analyzed with time series methods. The solution also provides visualizations to support further analyses.de
dc.identifier.doi10.18420/informatik2021-107
dc.identifier.isbn978-3-88579-708-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37611
dc.language.isode
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-314
dc.subjectMachine Learning Architecture
dc.subjectEnterprise Architecture
dc.subjectTime Series
dc.subjectOperations
dc.titleAn architecture for detecting infrastructure anomalies at Germany’s Federal Employment Agencyde
gi.citation.endPage1301
gi.citation.startPage1283
gi.conference.date27. September - 1. Oktober 2021
gi.conference.locationBerlin
gi.conference.sessiontitleWorkshop: (Agiles) Enterprise Architecture Management in Forschung und Praxis (EAM)

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P314 - INFORMATIK 2021 - Computer Science & Sustainability