Business process composition based on meta models

Self Cite

Business process is also a complex area which receives much attention in recent years especially in increasing productivity and saving cost. Meanwhile, situation at the company allows existing business processes to be enlarged. This paper proposed the performance measurement based on coloured petri net simulation of scalable business processes, which has purpose to compare the performance of scalable business processes. For experiments, this paper uses real-world business processes. Then compare it to some business processes that have been enlarged. The result shows that scalable business processes influence the performance of business process. This paper provides feedback to business process developers for determine appropriate business processes based on the performance through coloured petri net simulation.

Section: B Similarity Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Measurement Based on Coloured Petri Net Simulation of Scalable Business Processes

Fauzan¹,

Sarno²,

Yaqin³

2017

Self Cite

“…Nowadays, process modeling has become a concern. A process model can be used for analyzing executable processes in the information systems and understanding the behavior of executor in implementing the processes [1]- [3]. All research about process discovery, e.g.…”

Section: Introductionmentioning

confidence: 99%

CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities

Sungkono¹,

Sarno²

2017

Self Cite

An intentional process model is known to analyze processes deeply and provide recommendations for the upcoming processes. Nevertheless, the discovery of intentions is a difficult task because the intentions are not recorded in the event log, but they encourage the executable activities in the event log. Map Miner is the latest algorithm to depict the intentional process model. A disadvantage of this algorithm is the inability to determine strategies that contain same activities with the different sequence with other strategies. This disadvantage leads failure on the intentional process model. This research proposes an algorithm for discovering an intentional process model by considering the sequence of activities and CHMM (Coupled Hidden Markov Model). The probabilities and states of CHMM are utilized for the formation of the intentional process model. The experiment shows that the proposed algorithm with considering the sequence of activities gets an appropriate intentional process model. It also demonstrates that an obtained intentional process model using proposed algorithm gets the better validity than an intentional process model using Map Miner Method.

“…There are a lot of ways to represent process model, such as UML, Causal Net, BPEL, BPMN, EPC, PNML, etc [2]. Each type of model has different characteristic, such as Petri Net uses token to connect the activity in the business process model, while in Causal Net, the activity can be connected directly [3].…”

Section: Introductionmentioning

confidence: 99%

Discovering Process Model from Event Logs by Considering Overlapping Rules

Effendi

Sarno

2017

Self Cite

Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of the discovered process models is a must. Nowadays, using process execution data in the past, process models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete information, there are some cases that are overlapping in process model. Moreover, the rules which are generated from existing method are not suitable with the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.