Enhancing the Detection of Anomalous Artifact Operations in Temporal Structured Workflows

Workflows are widely used for automating business processes in various modern software architecture styles, including Service-Oriented Architecture (SOA), microservices, and serverless computing. Temporal structured workflows (TSWs) involve specifying the minimum and maximum execution time intervals for each activity. TSWs facilitate expressing the temporal aspects of business processes, such as task deadlines and durations. At the design stage of TSWs, excluding erroneous operations on artifacts (data objects) can help prevent errors and unexpected behavior during execution. However, TSWs are complex to analyze due to the intricate interplay between control flow, artifact operations, and temporal aspects. Existing approaches adopt the outcome of conventional, non-temporal, algorithms for detecting continuous anomalies, resulting in inaccuracies such as false positives and undetected anomalies specific to TSWs. To improve the analysis of TSWs, this paper presents an improved methodology based on an extended SP-tree structure called TSP-tree. It presents a set of definitions for temporal-aware anomalies in TSWs. Our approach involves two steps: first, transforming a TSW into a TSP-tree, and second, applying several algorithms to the TSP-tree to detect abnormal operations on artifacts. Compared with previous methods, our approach simplifies the analysis, detects more artifact anomalies, and improves the accuracy of detecting artifact anomalies within TSWs.