Hidden Markov Model for Process Mining of Parallel Business Processes

Sarno, Riyanarto; Sungkono, Kelly Rossa

doi:10.15866/irecos.v11i4.8700

Cited by 36 publications

(23 citation statements)

References 13 publications

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“…(4) Proposing the weight of the control-flow pattern which are based on logic table as a basis of our proposed similarity methods. (5) To prove that TPED and Cosine-TDP are able to differentiate the operators of parallel relationships compared to the existing method (GED and Cosine measure).…”

Section: Introductionmentioning

confidence: 98%

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

Wahyuni¹,

Sungkono²,

Sarno³

2019

IJIES

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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.

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Section: Introductionmentioning

confidence: 98%

“…There are several control-flow patterns [3] to depict operators of parallel relationships, i.e. AND, OR, and XOR [4,5]. The control-flow patterns can be modeled in different ways depend on the kinds of the graphical representations.…”

Section: Introductionmentioning

confidence: 99%

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

Wahyuni¹,

Sungkono²,

Sarno³

2019

IJIES

Self Cite

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“…Process models have been widely utilized for measuring the performance of processes in the company and pointing issues related with those processes [1]. Several areas of issues are deceptions [2][3], communications [4], and environments [5].…”

Section: Introductionmentioning

confidence: 99%

“…Process models can be drawn manually by analysts or can be generated based on the event logs. Many kinds of research, such as Ontology Invisible Task [6], Hidden Markov Model-Parallel (HMM-Parallel) [1], Coupled Hidden Markov Model-Invisible Task (CHMM-Invisible Task) [7], Alpha# [8], Alpha++ [9], Graphical method [10], and Time-based Alpha Miner [11] already suggested methods for discovering process models.…”

Section: Introductionmentioning

confidence: 99%

“…Time-Based Alpha Miner is a modification of the Alpha Miner [13] algorithm that considers the sequence of activities and time interval information from the event log to find parallel business process models. Afterward, HMM-Parallel [1] is another approach that uses the Baum-Welch method and the double time-stamped event log to discover parallel relationships. The development of HMM-Parallel [14] has been able to find models of processes related to the invisible task, but have not been able to handle non-free choices.…”

Section: Introductionmentioning

confidence: 99%

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Modified Alpha++ Algorithm for Discovering the Hybrid of Non-Free Choice and Invisible Task of Business Processes

Hakim¹,

Sarno²,

Sungkono³

2019

IJIES

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Process discovery is a concern in analyzing business processes. Process discovery methods handle many aspects, e.g. a hybrid of non-free choice and invisible task. A hybrid of non-free choice and an invisible task is a condition that needs additions of invisible tasks in non-free choice relationships. Process discovery methods, e.g. Alpha++, Alpha#, Graphical method, Time-based Alpha Miner, cannot discover the hybrid of non-free choice and invisible task, especially invisible tasks in the overlapping pattern. To modeling a hybrid of non-free choice and invisible tasks, this research proposes an algorithm of adding invisible tasks, including invisible tasks of overlapping pattern, automatically in the event log and processing the event log with the rules of Alpha++ algorithm. To test the proposed algorithm, namely Invisible Task Log with Alpha++, the experiment compares it with Alpha++, Alpha#, Graphical Method, Time-based Alpha Miner, Hidden Markov Model-Parallel, and Coupled Hidden Markov Model-Invisible Task. The experiment results proved that Invisible Task Log with Alpha++ algorithm obtains higher precision than the obtained precision by comparison methods.

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Coupled Hidden Markov Model for Process Discovery of Non-Free Choice and Invisible Prime Tasks

Sarno

Sungkono

2017

Procedia Computer Science

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Hidden Markov Model for Process Mining of Parallel Business Processes

Cited by 36 publications

References 13 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

Modified Alpha++ Algorithm for Discovering the Hybrid of Non-Free Choice and Invisible Task of Business Processes

Coupled Hidden Markov Model for Process Discovery of Non-Free Choice and Invisible Prime Tasks

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