A Method for Mining Process Models With Indirect Dependencies via Petri Nets

Sun, Huiming; Du, Yuyue; Qi, Liang; He, Zhaohan

doi:10.1109/access.2019.2923624

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…Many researchers have proposed a range of process mining algorithms. However, there exist many problems in the process mining algorithm, such as short loops [28], indirect dependency relationships [29], duplicated transitions, invisible transitions, noises, and infrequent behaviors [30]. Some early mining algorithms, such as the α-algorithm [8] and its derived improved algorithm [9,10], the ILP mining algorithm [11], the inductive miner algorithm [12], and the domain-based mining algorithm [13], disregarded noises in the event log.…”

Section: Related Workmentioning

confidence: 99%

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Wang

Fang

Asare

et al. 2021

Scientific Programming

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Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

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Section: Related Workmentioning

confidence: 99%

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Wang

Fang

Asare

et al. 2021

Scientific Programming

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“…Dependency measures are used to determine whether or not and which kind of dependency is present. A history dependency in our sense takes into account causal dependencies that are not only concerned with two consecutive activities but a small number of consecutive activities [8]. Process models visualizing history dependency prevent, in some case, unintended cycles as demonstrated in Fig.…”

Section: Related Workmentioning

confidence: 99%

Visually Representing History Dependencies in Event Logs

Wetzel

Koschmider

Wilke

2021

Lecture Notes in Business Information Processing

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Many process mining tools produce directly-follows graphs (DFG) as visual representations of event logs. While the "directly follows" relation is a good starting point for visualizations, there are simple phenomena it does not capture, for instance, when whether or not an event directly follows another event depends on the event directly preceding it. We call this a history dependency. This paper presents an empirical study of preferences for visualizing history dependencies: plain DFGs and two enhanced variants of DFGs (with additional arcs or rectangles) are evaluated. Our empirical study provides strong support for making an effort (to discover and) to explicitly visualize history dependencies. A ProM plug-in generating such explicit visualization is described in this paper.

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“…To achieve this, we have partially modified the technique of process model discovering, and also the α-algorithm itself [1,2,3] by introducing the relation of indirect precedence as another basic relation between the activities recorded in the event log [5,6]. Within that goal we firstly defined the so called causally complete event logs, which do not fulfil conditions of completeness, and yet using our modified algorithm α || -algorithm) we can reconstruct the original network of a parallel bussiness process.…”

Section: Introductionmentioning

confidence: 99%

Weakly Complete Event Logs in Process Mining

Lekić¹,

Milicev

2021

cai

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Many information systems have a possibility to record their execution, and, in this way, to generate a trace about events describing the real system behaviour. From behaviour example records in traces of the event log, the α-algorithm automatically generates a process model that belongs to a subclass of Petri nets, known as workflow nets. One of the basic limiting assumptions of α-algorithm is that the event log needs to be complete. As a result of attempting to overcome the problem of completeness of the event log, we introduced the notion of weakly complete event logs, from which our modified technique and algorithm can produce the same result as the α-algorithm from complete logs on parallel processes. Thereby weakly complete logs can be significantly smaller than complete logs, considering the number of traces they consist of. Weakly complete logs were used for the realization of our idea of interactive parallel business process model generation.

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A Method for Mining Process Models With Indirect Dependencies via Petri Nets

Cited by 9 publications

References 42 publications

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Visually Representing History Dependencies in Event Logs

Weakly Complete Event Logs in Process Mining

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