Mining decision to discover the relation of rules among decision points in a non-free choice construct

Sarno, Riyanarto; Sari, Putu Linda Indita; Sunaryono, Dwi; Amaliah, Bilqis; Mukhlash, Imam

doi:10.1109/icts.2014.7010557

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…When mining an event log, some process discovery algorithms are not able to represent a multiple-choice workflow correctly. Some researchers have identified the pattern as XOR-split, while others have identified it as AND-split [17]. Alpha ++, which uses Petri net for modeling [12], is not able to discover OR.…”

Section: Methodsmentioning

confidence: 99%

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Modified Time-Based Heuristics Miner for Parallel Business Processes

Sarno

Effendi

Haryadita

2016

IRECOS

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Process Mining, or Process Discovery, is a method for modeling the workflow of a business process from event logs. Business process models contain sequential and parallel traces. In this paper, a modification of the frequently used process-mining algorithm Heuristics Miner is proposed. The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log. It can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns; the latter cannot be discovered by the original Heuristics Miner. The threshold intervals are determined on the basis of the average dependency measure in the dependency graph. The experimental results show that the proposed algorithm is able to discover concurrent business processes formed by parallel (AND) and conditional (OR) patterns, whereas the existing Heuristics Miner algorithm can only discover concurrent business processes formed by parallel (AND) patterns. This paper also provides an evaluation of validity and fitness of the discovered process model.

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Section: Methodsmentioning

confidence: 99%

“…The positive observations threshold uses the minimum direct successor frequency from the direct successor frequency matrix [17], [20].…”

Section: Iii1 Mining the Dependency Graphmentioning

confidence: 99%

Modified Time-Based Heuristics Miner for Parallel Business Processes

Sarno

Effendi

Haryadita

2016

IRECOS

Self Cite

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“…Along with the development of computer technology is increasingly rapidly over the past few years, business process management technology is also increasingly being used [1], [2]. It was also in line with the number of existing business processes.…”

Section: Introductionmentioning

confidence: 99%

“…In the end, processing and in-depth analysis of the collection of existing business process models into a valuable knowledge. Processing and analysis can be done by applying data mining [2]. Data mining is the process of extracting important information or patterns in the databases.…”

Section: Introductionmentioning

confidence: 99%

“…The application of data mining in the business process model is expected to be able to gather information and analyze the results to improve the efficiency, notice the trend and determine the standardization of business processes. Based on previous studies [2], it is said that in measuring the similarity value of business processes used integration metric between semantic and structural similarity. The integration of semantic and structural similarity is done by weighting the value.…”

Section: Introductionmentioning

confidence: 99%

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Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

Mukhlash

Iqbal

et al. 2016

AIP Conference Proceedings

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Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth-Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes.

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A Method for Mining Process Models With Indirect Dependencies via Petri Nets

Sun

et al. 2019

IEEE Access

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Process mining aims to build the models of business processes and get valuable information according to event logs generated from enterprise information systems. There exist some indirect dependences, which refer to the relationship between discontinuous activities in business processes. However, the existing approaches cannot accurately identify such dependences from the event logs. Thus, this paper extends the α algorithm and proposes a new one named the α TR algorithm, which uses the association rules to describe the indirect dependences. First, an algorithm is proposed to identify the choice and loop structures in the business process. Then, the association rules are mined to describe the indirect dependences. Finally, we design an extended Petri net to formalize the process model, which can accurately describe the indirect dependences. The effectiveness of the proposed approach is illustrated by the experiments on ProM.

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Mining decision to discover the relation of rules among decision points in a non-free choice construct

Cited by 6 publications

References 12 publications

Modified Time-Based Heuristics Miner for Parallel Business Processes

Modified Time-Based Heuristics Miner for Parallel Business Processes

Mining fuzzy time interval sequential pattern on event log data using FP-Growth-Prefix-Span algorithms

A Method for Mining Process Models With Indirect Dependencies via Petri Nets

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