A Combination of the Evolutionary Tree Miner and Simulated Annealing

Nurlaili, Afina Lina; Sarno, Riyanarto

doi:10.11591/eecsi.v4.980

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The most notorious, among this type of approaches, are those based on evolutionary (genetic) algorithms [11,25]. However, several other metaheuristics have been explored, such as the imperialist competitive algorithm [1], the swarm particles optimization [18,29,44], and simulated annealing [31,58].…”

Section: Related Workmentioning

confidence: 99%

Advanced Process Discovery Techniques

Augusto

Carmona

Verbeek

2022

Lecture Notes in Business Information Processing

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Given the challenges associated to the process discovery task, more than a hundred research studies addressed the problem over the past two decades. Despite the richness of proposals, many state-of-the-art automated process discovery techniques, especially the oldest ones, struggle to systematically discover accurate and simple process models. In general, when the behavior recorded in the input event log is simple (e.g., exhibiting little parallelism, repetitions, or inclusive choices) or noise free, some basic algorithms such as the alpha miner can output accurate and simple process models. However, as the complexity of the input data increases, the quality of the discovered process models can worsen quickly. Given that oftentimes real-life event logs record very complex and unstructured process behavior containing many repetitions, infrequent traces, and incomplete data, some state-of-the-art techniques turn unreliable and not purposeful. Specifically, they tend to discover process models that either have limited accuracy (i.e., low fitness and/or precision) or are syntactically incorrect. While currently there exists no perfect automated process discovery technique, some are better than others when discovering a process model from event logs recording complex process behavior. In this chapter, we introduce four of such techniques, discussing their underlying approach and algorithmic ideas, reporting their benefits and limitation, and comparing their performance with the algorithms introduced in the previous chapter.

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Section: Related Workmentioning

confidence: 99%

Advanced Process Discovery Techniques

Augusto

Carmona

Verbeek

2022

Lecture Notes in Business Information Processing

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“…Some research studies adapted the particle swarm optimization metaheuristic to solve the problem of automated process discovery from event logs [15,17], but these studies are seminal and they lack of a solid evaluation on real-life logs. One of the most recent studies tried to combine evolutionary computation with particle swarm optimization [25] by extending the work of Buijs et al [13], but also in this case the authors did not provide a working implementation of their method, and they did not assess it on public datasets, so that it is difficult to estimate the real benefits of their proposed improvements. In our context, the main limitation of P-metaheuristics is that they are computationally heavy due to the cost of constructing a solution (i.e., a process model) and evaluating its accuracy.…”

Section: Optimization Metaheuristics In Automated Process Discoverymentioning

confidence: 99%

Optimization framework for DFG-based automated process discovery approaches

et al. 2021

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The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

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A Combination of the Evolutionary Tree Miner and Simulated Annealing

Cited by 2 publications

References 13 publications

Advanced Process Discovery Techniques

Advanced Process Discovery Techniques

Optimization framework for DFG-based automated process discovery approaches

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