Split miner: automated discovery of accurate and simple business process models from event logs

In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.

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“…Split miner consists of the following five steps [39]. First, it discovers a directly-follows dependency graph and detects short loops.…”

Section: Split Minermentioning

confidence: 99%

Decay Replay Mining to Predict Next Process Events

Theis

Darabi

2019

IEEE Access

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“…Throughout this paper, we use a state-of-the-art discovery algorithm called split miner [37,38] which is a recent technique to discover PNs from event logs. The method has been developed by Augusto et al with the objective to detect models with high fitness and precision, yet low complexity.…”

Section: Process Discovery Algorithmmentioning

confidence: 99%

Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

Theis

Darabi

2019

Proc. ACM Hum.-Comput. Interact.

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Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have developed a method which detects inefficient behavior assuming that at least one optimal HCI strategy is known. This method provides recommendations to gradually adapt users' behavior towards the optimal way of interaction considering satisfaction of users. Based on users' behavior logs tracked by a Java application suitable for multi-application and multi-instance environments, we demonstrate the applicability for a specific task in a common Windows environment utilizing realistic simulated behaviors of users.

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“…The discovered model can be beneficial, starting from inspection and finding valuable insights to observing the conformance with the reference model. Several methods are known for discovering business process models from event logs, including alpha miner [2], heuristic miner [3], inductive miner [4], fuzzy miner [5], split miner [6,7], and graphbased miner [8,9]. Graph-based miner algorithms outperform others in lower time complexity [10].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Waspada¹,

Sarno²,

Sungkono³

2020

IJIES

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The existing method of graph-based process model discovery has weaknesses in detecting parallel relationship (XOR, AND, and OR). The algorithm only works on a particular graph structure, so it must be reconfigured when applied to other different structures. To answer this problem, this paper proposes an improved method of parallel model detection, which is designed in two phases. The first one consists of three steps; firstly is to count and record the value of relationship frequency into every node in a graph model. Then, the second step implements the algorithm to discover the concurrent relationship. The third step detects all possible split and join relationships. Based on the first phase, then a consistent and robust parallel discovery algorithm can be developed. The first parallel algorithm is to identify the XOR relationship. This algorithm is designed with the rule that the XOR pattern cannot have a concurrent relationship between its branch nodes. Next, the algorithm for detecting AND and OR must detect the existence of any concurrent relationship in its branches. Then, AND and OR pattern is differentiated by their unique characteristic of relationship frequency at branch nodes. To verify the ability of the proposed methods in which the existing method fails, we have designed four scenarios. Scenario 1 and 2 consecutively were arranged with two and three branches parallel model. Scenario 3 located the AND and OR inside the XOR pattern. In scenario 4 the sequence relationships were inserted between split and join of parallel patterns. The experimental results show that the proposed method successfully recognizes and differentiates XOR, AND and OR patterns correctly in all scenarios. It also sounds in all discovered model and get 100% fitness.

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Split miner: automated discovery of accurate and simple business process models from event logs

Cited by 160 publications

References 38 publications

Decay Replay Mining to Predict Next Process Events

Decay Replay Mining to Predict Next Process Events

Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

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