Analyzing control flow information to improve the effectiveness of process model matching techniques

2017 ◽  
Vol 100 ◽  
pp. 6-14 ◽  
Author(s):  
Christopher Klinkmüller ◽  
Ingo Weber
Keyword(s):  
Process Model ◽  
Control Flow ◽  
Model Matching ◽  
Flow Information ◽  
Matching Techniques

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

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

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.

scholarly journals A probabilistic evaluation procedure for process model matching techniques

2018 ◽  
Vol 117 ◽  
pp. 393-406 ◽  
Author(s):  
Elena Kuss ◽  
Henrik Leopold ◽  
Han van der Aa ◽  
Heiner Stuckenschmidt ◽  
Hajo A. Reijers
Keyword(s):  
Process Model ◽  
Evaluation Procedure ◽  
Model Matching ◽  
Probabilistic Evaluation ◽  
Matching Techniques

scholarly journals Probabilistic Evaluation of Process Model Matching Techniques

2016 ◽  
pp. 279-292 ◽  
Author(s):  
Elena Kuss ◽  
Henrik Leopold ◽  
Han van der Aa ◽  
Heiner Stuckenschmidt ◽  
Hajo A. Reijers
Keyword(s):  
Process Model ◽  
Model Matching ◽  
Probabilistic Evaluation ◽  
Matching Techniques

scholarly journals Deep Analysis of Process Model Matching Techniques

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 99239-99253
Author(s):  
Muhammad Ali ◽  
Khurram Shahzad ◽  
Syed Irtaza Muzaffar ◽  
Muhammad Kamran Malik
Keyword(s):  
Process Model ◽  
Model Matching ◽  
Matching Techniques

Compact memetic algorithm-based process model matching

2018 ◽  
Vol 23 (13) ◽  
pp. 5249-5257
Author(s):  
Xingsi Xue
Keyword(s):  
Process Model ◽  
Memetic Algorithm ◽  
Model Matching

A Graph-Based Approach for Semantic Process Model Discovery

2011 ◽  
pp. 438-462 ◽  
Author(s):  
Ahmed Gater ◽  
Daniela Grigori ◽  
Mokrane Bouzeghoub
Keyword(s):  
Web Services ◽  
Process Model ◽  
Service Oriented Architecture ◽  
Graph Matching ◽  
Process Models ◽  
Matching Problem ◽  
Service Oriented ◽  
Semantic Process ◽  
Process Structure ◽  
Matching Techniques

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.

A Novel Approach for Business Process Model Matching Using Genetic Algorithms

2020 ◽  
Vol 12 (1) ◽  
pp. 1-19
Author(s):  
Mostefai Abdelkader ◽  
Ignacio García Rodríguez de Guzmán
Keyword(s):  
Genetic Algorithms ◽  
Business Process ◽  
Process Model ◽  
Optimization Problem ◽  
Process Models ◽  
Similarity Metrics ◽  
Model Matching ◽  
Matching Problem ◽  
Novel Approach ◽  
The One

This paper formulates the process model matching problem as an optimization problem and presents a heuristic approach based on genetic algorithms for computing a good enough alignment. An alignment is a set of not overlapping correspondences (i.e., pairs) between two process models(i.e., BP) and each correspondence is a pair of two sets of activities that represent the same behavior. The first set belongs to a source BP and the second set to a target BP. The proposed approach computes the solution by searching, over all possible alignments, the one that maximizes the intra-pairs cohesion while minimizing inter-pairs coupling. Cohesion of pairs and coupling between them is assessed using a proposed heuristic that combines syntactic and semantic similarity metrics. The proposed approach was evaluated on three well-known datasets. The results of the experiment showed that the approach has the potential to match business process models effectively.

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