Runtime defect prediction of industrial business processes: A focused look at real-life SAP systems

Abstract

Business process operations are the dominant logic underpinning most of the service-based applications currently in use. Situated in the field of SAP business processes — commonly referred to as iFlows — and their integration, this paper looks into the defectiveness of such flows with a Machine-Learning approach. We propose to cluster and classify at runtime the Integration Flows of business processes during their orchestration; we do so by using metrics extracted from the Integration of 400+ complex business interaction and service orchestration Flows along with their metadata. Through a combined ensemble-based, clustering, and supervised learning exercise, we conclude that an AI-based approach for runtime defect prediction of iFlows shows considerable promise in providing actionable insights for better orchestration intelligence, especially in sight of self-aware business processes of the future.