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Open Access December 26, 2021

Rule-Based Automation for IT Service Management Workflows

Abstract The automation of IT Service Management (ITSM) workflows using explicit rules and data has been established for years. Domain-specific rule engines interpret rules written in declarative rule modelling languages and generate forwarding arrows to process event streams and support decision making. Such automation is augmented by rule-driven Quality Assurance for correctness, safety, and risk [...] Read more.
The automation of IT Service Management (ITSM) workflows using explicit rules and data has been established for years. Domain-specific rule engines interpret rules written in declarative rule modelling languages and generate forwarding arrows to process event streams and support decision making. Such automation is augmented by rule-driven Quality Assurance for correctness, safety, and risk management. The service desk is the onshore base of an ITSM supply chain. An end-to-end incident response service resolves incidents using only onshore resources and employs back office teams to help with unresolvable incidents. The forward factories of rule-based automation for ticket processing service are identified. Several rule-based workflows in incident and change management have been published. Further glimpses of the future across all ITSM workflows are provided based on training in an online ITSM service with automated operations. Rule engines are specialised components that direct the processing of data flows according to pre-defined rules. Decision factories complement the more common event-driven rule engines. While event processing occurs below the polling frequency of the source, rules in decision factories are triggered based on the arrival of data. These factories are applied in ITSM for risk and safety evaluation and quality assurance. Rule-enriched architectures incorporate domain-specific modelling languages to ensure correctness with respect to qualitative quality attributes. Dedicated factories provide resilience, detect slack or over-utilisation, and offer point-in-time assurance and testing.
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Keyword:  Event-Driven Rule Processing

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