DisCoveR: Accurate & Efficient Discovery of Declarative Process Models

Autor:	Back, Christoffer Olling, Slaats, Tijs, Hildebrandt, Thomas Troels, Marquard, Morten
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Formal Languages and Automata Theory Computer Science - Software Engineering Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Declarative process modeling formalisms - which capture high-level process constraints - have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an extremely efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We precisely formalize the algorithm, describe a highly efficient bit vector implementation and rigorously evaluate performance against two other declarative miners, representing the state-of-the-art in Declare and DCR Graphs mining. DisCoveR outperforms each of these w.r.t. a binary classification task, achieving an average accuracy of 96.2% in the Process Discovery Contest 2019. Due to its linear time complexity, DisCoveR also achieves run-times 1-2 orders of magnitude below its declarative counterparts. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool, discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users. Comment: Author's original version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.10085 Zobrazit plný text záznamu View this record from Arxiv