Business Process and Risk Models Enrichment: Considerations for Business Intelligence

Sienou, Amadou; Karduck, Achim P.; Lamine, Elyes; Pingaud, Hervé

doi:10.1109/icebe.2008.123

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Gantt chart will show the relations of activities. Next, a process model is created based on the Gantt chart for all cases in Petri Net [3][20] [22]. We need to create the transition, input, output and place for all the process model [23].…”

Section: Integrated Discovery Approachmentioning

confidence: 99%

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Process Discovery of Business Processes Using Temporal Causal Relation

Effendi

Nuzulita

2019

JISEBI

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Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

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Section: Integrated Discovery Approachmentioning

confidence: 99%

“…Nowadays, enterprise computing manages business processes which has grown up rapidly [1][2] [3]. This situation triggers the production of a massive event log that gives knowledge about the activities of business processes run recently or few years ago [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Process Discovery of Business Processes Using Temporal Causal Relation

Effendi

Nuzulita

2019

JISEBI

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“…To aid our evaluation, Table 2 is provided to summarize the types of riskrelated activities that each of the twenty-seven approaches supports at each stage of the BPM lifecycle. Jakoubi, and Quirchmayr, 2008b;Tjoa, Jakoubi, Goluch, and Kitzler, 2010;Tjoa, Jakoubi, Goluch, and Quirchmayr, 2008a;Tjoa et al, 2011;Jakoubi, Tjoa, and Quirchmayr, 2007;Kitzler, 2010a, 2010b;Jakoubi, Neubauer, and Tjoa, 2009b;Ekelhart, Fenz, Klemen, and Weippl, 2007;Jakoubi and Tjoa, 2009;Jakoubi, Goluch, Tjoa, and Quirchmayr, 2008;Goluch et al, 2008 Sienou et al DI02 Sienou, Lamine, Pingaud, and Karduck, 2010;Sienou, Lamine, Karduck, and Pingaud, 2008b;Sienou, Lamine, Karduck and Pingaud, 2007;Sienou, Lamine, Pingaud, and Karduck, 2009;Sienou, Lamine, and Pingaud, 2008c;Sienou, Karduck, and Pingaud, 2006;Sienou, Karduck, Lamine, and Pingaud, 2008a;Karduck, Sienou, Lamine, and Pingaud, 2007;Sienou, 2009 Rotaru et al DI08 Rotaru, Wilkin, Churilov, and Neiger, 2008;Neiger, Churliov, zur Muehlen, and Rosemann, 2006;Rotaru, Wilkin, Churilov, Neiger, and Ceglowski, 2009 Betz et al DI09 Betz, Hickl, and Oberweis, 2011 Herrmann and Herrmann…”

Section: Literature Evaluationmentioning

confidence: 99%

Current Research in Risk-aware Business Process Management―Overview, Comparison, and Gap Analysis

Suriadi

Weiß²,

Winkelmann³

et al. 2014

CAIS

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The management of risk in business processes has been the subject of active research in the past few years. Potentially, many benefits can be obtained by integrating the two traditionally separated fields of risk management and business process management, including the ability to minimize risks in business processes by design and to mitigate such risks at run time. While there has been an increasing amount of research aimed at delivering such an integrated system, these research efforts vary in terms of scope, goals, and functionality. Through the systematic collection and evaluation of relevant literature, this article compares and classifies current approaches in the area of risk-aware business process management in order to expose and explain current research gaps. The process through which relevant literature was collected, filtered, and evaluated is also detailed. Finally, a research agenda is proposed.

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“…An organization has information systems which are used to collect, store and process data to gain information on certain things [1,2]. They can also support management and decision making of business processes [3]. Systems in the organization will generate event log which gives knowledge about the activities of business processes run recently or few years ago [4,5].…”

Section: Introductionmentioning

confidence: 99%

Time-based α+ miner for modelling business processes using temporal pattern

Effendi

Sarno

2020

TELKOMNIKA

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Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based α+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based α+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original α+ Miner algorithm.

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Business Process and Risk Models Enrichment: Considerations for Business Intelligence

Cited by 6 publications

References 3 publications

Process Discovery of Business Processes Using Temporal Causal Relation

Process Discovery of Business Processes Using Temporal Causal Relation

Current Research in Risk-aware Business Process Management―Overview, Comparison, and Gap Analysis

Time-based α+ miner for modelling business processes using temporal pattern

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