Fast Synthetic Data-Aware Log Generation for Temporal Declarative Models

Abstract

Business Process Management algorithms are heavily limited by suboptimal algorithmic implementations that cannot leverage state-of-the-art algorithms in the field of relational and graph databases. The recent interest in this discipline for various IT sectors (cybersecurity, Industry 4.0, and e-Health) calls for defining new algorithms improving the performance of existing ones. This paper focuses on generating several traces collected in a log from declarative temporal models by pre-emptively representing those as a specific type of finite state automaton: we show that this task boils down to a single-source multi-target graph traversal on such automaton where both the number of distinct paths to be visited as well as their length are bounded. This paper presents a novel algorithm running in polynomial time over the size of the declarative model represented as a graph and the desired log’s size. The final experiments show that the resulting algorithm outperforms the state-of-the-art data-aware and dataless sequence generations in business process management.