Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques, that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.
Recent approaches for managing Enterprise Architecture (EA) models provide technical systems to procure information from existing repositories within the application landscape of an organization. Beyond technical solutions, social factors are of utmost importance to implement a successful EA initiative. Institutional theory has for example been employed to understand crucial factors for realizing EA management (EAM) benefits through architectural thinking. Yet, it remains unclear how these social factors influence a federated approach for EA model management. Based on a socio-technical systems perspective, we investigate success factors for Federated EA Model Management (FEAMM) by conducting qualitative interviews with industry experts. Our findings suggest that success factors for FEAMM are related to the model sources, modeling instruments, and model integration aspects from a technical perspective as well as to organizational grounding, governance, enforcement, efficiency, goal alignment, and trust from a social perspective.
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