Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

Lehto, Teemu; Hinkka, Markku

doi:10.1007/978-3-030-53337-3_18

Cited by 5 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, none of them provides a method for feature recommendation. Yet, there is some work on influence analysis that aims at finding feature values that correlate with a specific property of the process [13][14][15]. These methods utilize the frequency of the concurrence of each one of the process feature values with the problematic cases to determine their influence.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, we have considered Fig. 13 The heat map of the correlations between the situation features and the discovered PAG using implemented plugin for the synthetic event logs generated such that the true SEMs are EQ 2 (the top figures), EQ 3 (the middle figures), and EQ 4 (the button figures) Fig. 14 The time needed for causal structure learning in milliseconds for the whole situation feature table, and the trimmed situation feature table using the situation features selected by SFVPR, SFSRF, and SFSBC…”

Section: Time and Quality Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Feature recommendation for structural equation model discovery in process mining

Qafari

Aalst

2022

Prog Artif Intell

View full text Add to dashboard Cite

Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today’s companies, the task of discovering the set of features that should be considered in causal analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible causal effect on the problem. The causal analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for causal analysis. We have implemented the proposed method as a plugin in ProM, and we have evaluated it using real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Time and Quality Evaluationmentioning

confidence: 99%

Feature recommendation for structural equation model discovery in process mining

Qafari

Aalst

2022

Prog Artif Intell

View full text Add to dashboard Cite

show abstract

“…However, none of them provides a method for feature recommendation. Yet, there is some work on influence analysis that aims at finding such a set of features [6,7,8].…”

Section: Related Workmentioning

confidence: 99%

Feature Recommendation for Structural Equation Model Discovery in Process Mining

Qafari¹,

Aalst²

2021

Preprint

View full text Add to dashboard Cite

Process mining techniques can help organizations to improve the operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today's companies, the task of discovering the set of features that should be considered in root cause analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible effect on the problem. The root cause analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for root cause analysis. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.

show abstract

“…Te use of text mining aims to improve the process discovery technique to extract more useful insights from data processing. Te latest approach to track clustering is a study conducted in [43] by detecting the business areas that are most correlated with the clusters found.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering

Prasetyo

Sarno

Wijaya

et al. 2023

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

The current business environment has no room for inefficiency as it can cause companies to lose out to their competitors, to lose customer trust, and to experience cost overruns. Business processes within the company continue to grow and cause them to run more complex. The large scale and complexity of business processes pose a challenge in improving the quality of process model because the effectiveness of time and the efficiency of existing resources are the biggest challenges. In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. Meanwhile, in the event log data, not only the activities but also the other resources, such as records of employee or staff working time, process service time, and processing costs, are recorded. This article proposes a mechanism alternative to optimize business processes by exploring the resources that occur in the process. The mechanism is carried out in three stages. The first stage is optimizing the job shop scheduling method from the generated event log. Scheduling the time becomes a problem in the job shop. Utilizing the right time can increase the effectiveness of performance in order to reduce costs. Scheduling can be defined as the allocation of multiple jobs in a series of machines, in which each machine only does one job at a time. In general, scheduling becomes a problem when sequencing the operations and allocating them into specific time slots without prolonging the technical and capacity constraints. The second stage is generating the resource value that is recorded in the event log from the results of analysis of the previous stage, namely, job shop scheduling. The resource values are multivariate and then clustered to determine homogeneous clusters. The last stage is optimizing the nonlinear multipolynomials in the homogeneous cluster formed by using the Hessian solution. The results obtained are analyzed to get recommendations on business processes that are appropriate for the company’s needs. The impact of long waiting times will increase service costs, but by improving workload, costs can be reduced. The process model and the value of service costs resulting from the mechanism in the research can be a reference for process owners in evaluating and improving ongoing processes.

show abstract

Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

Cited by 5 publications

References 39 publications

Feature recommendation for structural equation model discovery in process mining

Feature recommendation for structural equation model discovery in process mining

Feature Recommendation for Structural Equation Model Discovery in Process Mining

Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering

Contact Info

Product

Resources

About