Decision mining for multi choice workflow patterns

Sarno, Riyanarto; Sari, Putu Linda Indita; Ginardi, Raden Venantius Hari; Sunaryono, Dwi; Mukhlash, Imam

doi:10.1109/ic3ina.2013.6819197

Cited by 28 publications

(13 citation statements)

References 12 publications

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“…The work presented in [19] considers the inclusive and exclusive decision rules discovery, but the technique needs a manual modification of the petri net model which is not practical when dealing with complex processes. Therefore, authors assume that these works are not relevant for the proposition and consider the works presented in [9] and [17] which allow the discovery of both inclusive and exclusive decision rules.…”

Section: Discussionmentioning

confidence: 99%

“…This technique is implemented in the multi-perspective explorer plug-in [18] of the ProM framework. In [19], authors propose an approach to explore inclusive decision rules using the Decision point analysis [8]. The approach consists in manually modifying the petri net model by transforming the "Or" gateway into an "And" gateway followed by a "Xor" gateway in each of its outgoing arcs.…”

Section: Decision Miningmentioning

confidence: 99%

See 1 more Smart Citation

On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

Es-Soufi

Yahia

Roucoules

2016

IFIP Advances in Information and Communication Technology

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. Currently, organizations tend to reuse their past knowledge to make good decisions quickly and effectively and thus, to improve their business processes performance in terms of time, quality, efficiency, etc. Process mining techniques allow organizations to achieve this objective through process discovery. This paper develops a semi-automated approach that supports decision making by discovering decision rules from the past process executions. It identifies a ranking of the process patterns that satisfy the discovered decision rules and which are the most likely to be executed by a given user in a given context. The approach is applied on a supervision process of the gas network exploitation.

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Section: Discussionmentioning

confidence: 99%

Section: Decision Miningmentioning

confidence: 99%

On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

Es-Soufi

Yahia

Roucoules

2016

IFIP Advances in Information and Communication Technology

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“…Perhaps the most visible implementation of this idea in the field of Process Mining is the Decision Miner [8]. The same concept is also applied in [14], yet focusing just on the multi-choice workflow pattern [15] case. In [2], the same concept is generalized by proposing a generic framework according to which virtually any characteristics can be correlated (again through decision trees classification) to another one.…”

Section: Related Workmentioning

confidence: 99%

Discovering Characteristics that Affect Process Control Flow

Delias

Grigori

Mouhoub

et al. 2015

Decision Support Systems IV – Information and Knowledge Management in Decision Processes

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Abstract. In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases' characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.

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“…Process mining is a discipline for discovering a process model by observing the events from information systems automatically [1]. The discovered process models are guidances for analyzing performance of processes in the applications [2], [3]. The discovered process models are also guidances for finding solution of complex issues in activities.…”

Section: Introductionmentioning

confidence: 99%

Coupled Hidden Markov Model for Process Mining of Invisible Prime Tasks

Sarno

Sungkono

2016

IRECOS

Self Cite

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Process mining provides process improvement in a variety of application domains. A primary focus of process mining is transferring information from event logs into process model. One of the issues of process mining is dealing with invisible prime tasks. An invisible prime task is an additional task in the process model to assist in showing real processes. However, a few of algorithm solves the issue. This research proposes an algorithm for dealing with invisible prime tasks. The proposed algorithm contains rules and equations utilizing probability of state transition of Coupled Hidden Markov and double time-stamped in event logs. The rules and equations are used for determining invisible prime tasks and parallel control-flows patterns. In addition to dealing with invisible prime tasks, the experiment results also show that the proposed algorithm obtains right parallel control-flow patterns from non-complete event logs. This proposed algorithm also decreases usage of the invisible prime task in A# algorithm without reducing the quality of discovered process models. It has proven with the fitness of process models obtained by the proposed algorithm are relatively high as those obtained by A# algorithm.

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Decision mining for multi choice workflow patterns

Cited by 28 publications

References 12 publications

On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

Discovering Characteristics that Affect Process Control Flow

Coupled Hidden Markov Model for Process Mining of Invisible Prime Tasks

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