Decision mining for multi choice workflow patterns

Decision mining is combination of process mining and machine learning technique to retrieve information about how an attribute in a business process affects a case's route choice. It identifies decision point by looking for XOR-splits in petri-net workflow model and analyzing rules for each choice based on available attributes using decision tree. Problem emerges when decision mining technique is used on a workflow that does not use either XOR or AND splits, for example OR-split gateway logic. OR-split does not have explicit representation in petri nets and it makes decision mining algorithm cannot find its decision point. Workflow pattern that uses OR-split as its splitting logic is multi choice. Multi choice does not have its own explicit representation in form of petri net and it is problematic to apply decision mining to this workflow pattern. To make multi choice can be analyzed by decision miner some modification needs to be applied to the petri net representation of this pattern. This paper proposes modification of OR-split gateway representation in petri net. The new representation of OR-split uses combination the existing XOR-split and AND-split to make the model easier to be analyzed using decision miner. The proposed modification do not affect the conformance of event log and process model, but will allow each choice branch to be checked by decision miner.

[1]  Jia-Lang Seng,et al.  An analytic approach to select data mining for business decision , 2010, Expert Syst. Appl..

[2]  Mike Wright,et al.  Petri net-based modelling of workflow systems: An overview , 2001, Eur. J. Oper. Res..

[3]  van der Wmp Wil Aalst,et al.  Workflow control-flow patterns : a revised view , 2006 .

[4]  van der Wmp Wil Aalst,et al.  Decision mining in business processes , 2006 .

[5]  Alex Alves Freitas,et al.  Inducing decision trees with an ant colony optimization algorithm , 2012, Appl. Soft Comput..

[6]  Katalina Grigorova Process modelling using Petri nets , 2003, CompSysTech '03.

[7]  B. Chandra,et al.  Moving towards efficient decision tree construction , 2009, Inf. Sci..

[8]  Duoqian Miao,et al.  Hierarchical decision rules mining , 2010, Expert Syst. Appl..

[9]  Dimitrios Gunopulos,et al.  Improving process models by discovering decision points , 2007, Inf. Syst..

[10]  Wil M. P. van der Aalst,et al.  Decision Mining in ProM , 2006, Business Process Management.

[11]  Wil M. P. van der Aalst,et al.  Pattern-based analysis of BPMN , 2005 .

[12]  Wil M. P. van der Aalst,et al.  Data-aware process mining: discovering decisions in processes using alignments , 2013, SAC '13.

[13]  C. Apte,et al.  Data mining with decision trees and decision rules , 1997, Future Gener. Comput. Syst..

[14]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[15]  William Acar,et al.  A Petri Net model view of decision making: an operational management analysis , 1995 .