Data‐driven business process similarity

Amiri, Mohammad Javad; Koupaee, Mahnaz

doi:10.1049/iet-sen.2016.0256

Cited by 14 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Business process models can be compared to evaluate its structure [17,18] and behavior [19,20] that have similar functions [21]. The comparison is called a business process similarity [22]. In calculating similarity between process models, structural [23], behavioral [23], or semantic similarity can be applied [24].…”

Section: Introductionmentioning

confidence: 99%

Novel Parallel Business Process Similarity Methods Based on Weighted-Tree Declarative Pattern Models

Wahyuni¹,

Sungkono²,

Sarno³

2019

IJIES

View full text Add to dashboard Cite

Existing methods, such as Graph Edit Distance (GED) and Cosine measure, still have drawback in obtaining similarity of parallel relationships by neglecting the control-flow patterns, i.e. AND, OR, and XOR. Since AND > OR > XOR, the similarity value of AND versus OR is greater than XOR versus OR and AND versus XOR. This paper proposes two new similarity methods, Tree Declarative Pattern Edit Distance (TPED) and Cosine-Tree Declarative Pattern (Cosine-TDP). They provides value to the control-flow pattern so the value of similarity can be seen more differently. The new methods utilize tree model of the declarative pattern. The results show that the proposed methods are better at differentiating parallel relationships than the existing methods, GED and Cosine measure. In obtaining AND versus OR, XOR versus OR, and AND versus XOR, TPED obtained 0.821, 0.811, and 0.78 while Consine-TDP obtained 0.834, 0.826, and 0.693. Meanwhile, GED obtained 1 for all parallel relationships whereas Cosine measure obtained 0.02, 0.08, and 0.04.

show abstract

Section: Introductionmentioning

confidence: 99%

Novel Parallel Business Process Similarity Methods Based on Weighted-Tree Declarative Pattern Models

Wahyuni¹,

Sungkono²,

Sarno³

2019

IJIES

View full text Add to dashboard Cite

show abstract

“…Recent attempts to consider data in business process management and the support of data modeling in business process standards have led to the creation of multiple business models with data access [7,26]. A systematic datadriven approach is proposed to assist situational application development and extract certain information from multiple sources to abstract service capabilities and represent in terms of tags [8].…”

Section: Workflow Similarity Assessmentmentioning

confidence: 99%

“…Generally, the similarity of workflows (or business processes) can be effectively calculated to evaluate whether a workflow can be replaced (or reused) by another when necessary. These techniques can be categorized into annotation-based [5], structure-based [6], and data-driven [7,8] mechanisms. We have proposed a layer-hierarchial technique for computing the similarity of scientific workflows in the myExperiment repository, where a layer hierarchy represents the relations between a workflow, its sub-workflow and their activities [9].…”

Section: Introductionmentioning

confidence: 99%

Topic-based crossing-workflow fragment discovery

Zhou

Wen

Wang

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

“…Similarity search, the practice of comparing a particular object or process to a collection of objects to identify those exhibiting close resemblance, has evolved into a contemporary topic bolstering business process management [1]. The detection of similarities in business processes facilitates the identification of redundant activities, thereby optimizing resources [2].…”

Section: Introductionmentioning

confidence: 99%

Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes

Trabelsi,

Khtira,

El Asri

2023

ISI

View full text Add to dashboard Cite

The detection, quantification, and scrutiny of redundancies within business processes is pivotal in achieving cost reduction, enhancing efficiency, and ensuring compliance. Redundancies, often leading to inefficiencies, result in escalated costs and errors, thereby detrimentally influencing an organization's overall performance. To counter these issues, data mining and process mining techniques offer promising solutions by identifying and analyzing process redundancies. Data mining, an approach devoted to the analysis of large datasets in order to discern patterns, relationships, and anomalies, has been applied to business processes. It provides insights into redundancies by scrutinizing process-related data, such as event logs, thereby revealing patterns in task executions that may indicate redundancies. In contrast, process mining employs event logs to generate a process model mirroring the actual execution of a process. This actual process model is subsequently contrasted against an expected process model, facilitating the identification of redundancies such as unnecessary activities or loops. Cluster analysis, a technique employed in both data mining and process mining, is exemplified for its capacity to group similar process instances or models based on specified attributes or characteristics. The application of cluster analysis aids in the identification of redundant process models or similar process patterns, thereby enabling further comparison and optimization.

show abstract

Data‐driven business process similarity

Cited by 14 publications

References 40 publications

Novel Parallel Business Process Similarity Methods Based on Weighted-Tree Declarative Pattern Models

Novel Parallel Business Process Similarity Methods Based on Weighted-Tree Declarative Pattern Models

Topic-based crossing-workflow fragment discovery

Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes

Contact Info

Product

Resources

About