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

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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.

Section: Introductionmentioning

confidence: 99%

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

Wahyuni¹,

Sungkono²,

Sarno³

2019

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“…One reason for adding invisible tasks is accommodating Split and Join relations in parallel processes. There are several algorithms that handle invisible tasks, such as Alpha# [9], Alpha$ [10], Coupled Hidden Markov Model-Nonfree Choice Invisible Task (CHMM-NCIT) [2] and Coupled Hidden Markov Model-Invisible Task (CHMM-IT) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Those checking processes prolongs the processing time of Alpha#. Furthermore, both of Coupled Hidden Markov Model-Nonfree Choice Invisible Task (CHMM-NCIT) [2] and Coupled Hidden Markov Model-Invisible Task (CHMM-IT) [5] use Coupled Hidden Markov Model (CHMM) and utilize Baum-Welch algorithm to determine the weights of variables of CHMM. However, those algorithm check sequences of the event log and both of hidden variables and observed variables, so the time complexities of CHMM-NCIT and CHMM-IT are high.…”

Section: Introductionmentioning

confidence: 99%

“…The fitness value of the obtained process model by using the proposed graph-database verifies the contribution. [2] The time complexity of the graph-based algorithm is low because the graph-based algorithm uses a graph-database that stores not only activities but also their relationships. The additional of invisible task is not can be checked directly from the relationship without looking for possible relationships between activities continuously.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Based Algorithms for Discovering a Process Model Containing Invisible Tasks

Sarno¹,

Sungkono²,

Johanes³

et al. 2019

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An event log records the business processes of a company. Modeling event logs aim to help users in analyzing business processes. One of the problems in modeling event logs automatically is the addition of invisible tasks. Invisible tasks are dummy activities, other than activities of an event log, that are added to a process model to describe a correct process model. This research proposes a graph-based algorithm to mine the data from an event log. From the data, the graph-based algorithm establishes an additional-invisible-task process model by converting all of the processes in the event log into a link list and adding invisible tasks and operators for parallel relations, such as XOR Split or XOR Join. The experimental analysis explains that the fitness of the discovered process models by the graph-based algorithm was as high as that of compared algorithms, such as Alpha# models, Alpha$ models, CHMM-NCIT models, and CHMM-IT models. Furthermore, the graph-based algorithm is more efficient than existing algorithms. This was proven by the time complexity of the graph-based, which is O(n 2 ) while both of Alpha# and Alpha$ algorithm have a time complexity of O(n 4 ) and both of CHMM-IT and CHMM-NCIT algorithm have O(n 3 ).

“…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%

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

Hakim¹,

Sarno²,

Sungkono³

2019

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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.