A Behavioral Similarity Measure between Labeled Petri Nets Based on Principal Transition Sequences

Wang, Jianmin; He, Tengfei; Wen, Lijie; Wu, Nianhua; Hofstede, Arthur H. M. ter; Su, Jianwen

doi:10.1007/978-3-642-16934-2_27

Cited by 37 publications

(16 citation statements)

References 19 publications

(44 reference statements)

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“…Measuring similarities among process models can be used for finding changes of a business process quickly, reusing process conveniently or discovering service satisfying specific user requirements rapidly (Becker & Laue, ). Gerke et al () measure the process similarity by counting the same subsequences between the traces in their logs while Wang et al () taking the problem of infinite traces and infinite set of traces into account. De Medeiros et al () present a method to calculate the similarity of process models based on comparing traces obtained from actual process executions or by simulation.…”

Section: Related Workmentioning

confidence: 99%

“…It is common that the process model of the continuously improved online business process system turns to be out of date. In order to find the most similar process models in the repository, you could check the similarity between its runtime log and one existing model's generated log (De Medeiros, Aalst, & Weijters, ; Dong, Wen, Huang, & Wang, ; Gerke, Cardoso, & Claus, ; Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

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A novel completeness definition of event logs and corresponding generation algorithm

Wen

et al. 2020

Expert Systems

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As the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel completeness definition of event logs and corresponding generation algorithm

Wen

et al. 2020

Expert Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…), which has limited its application a lot. To represent the process models whose behavioral features have infinite traces in a finite way, Wang et al [12] proposed a principle transition sequences based measure, which is denoted as PTS-measure. It is realized by constructing the coverability tree of a Petri net model and this method requires traversing all state space, which may lead to the so-called state space explosion problem.…”

Section: Related Workmentioning

confidence: 99%

Towards Comprehensive Support for Business Process Behavior Similarity Measure

Liu

Zeng

Duan

et al. 2019

IEICE Trans. Inf. & Syst.

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Business process similarity measure is required by many applications, such as business process query, improvement, redesign, and etc. Many process behavior similarity measures have been proposed in the past two decades. However, to the best of our knowledge, most existing work only focuses on the direct causality transition relations and totally neglect the concurrent and transitive transition relations that are proved to be equally important when measuring process behavior similarity. In this paper, we take the weakness of existing process behavior similarity measures as a starting point, and propose a comprehensive approach to measure the business process behavior similarity based on the so-called Extended Transition Relation set, ETR-set for short. Essentially, the ETR-set is an extended transition relation set containing direct causal transition relations, minimum concurrent transition relations and transitive causal transition relations. Based on the ETR-set, a novel process behavior similarity measure is defined. By constructing a concurrent reachability graph, our approach finds an effective technique to obtain the ETR-set. Finally, we evaluate our proposed approach in terms of its property analysis as well as conducting a group of control experiments.

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“…Gerke et al [19] , Wang et al [20] compare the compliance between BPs calculating the longest common subsequence of traces, i.e., similarity degree of ordering rules of activities between two BPs. However, this approach is computational expensive when there are large sets of traces.…”

Section: Related Workmentioning

confidence: 99%

TrazasBP: A Framework for Business Process Models Discovery Based on Execution Cases

Ordóñez¹,

Ordóñez²,

Buchelli³

et al. 2018

Polibits

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Execution of business processes generates data which are commonly recorded in logs. Historical information of execution cases may be used for recommending future execution paths. This is useful when the control flow of the process is not known by the user. This paper presents TrazasBP, a framework for BP indexing and searching based on execution cases. TrazasBP indexes BPs based on execution cases (traces) retrieved from log files. TrazasBP not only takes into account the textual information of BP elements, but also the causal dependence between these elements. Furthermore, due to its low computational cost, TrazasBP may be used as indexing mechanism in order to reduce the search space. Experimental evaluation shows promising values of graded precision, recall and F-measure when compared with results obtained from human search.

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A Behavioral Similarity Measure between Labeled Petri Nets Based on Principal Transition Sequences

Cited by 37 publications

References 19 publications

A novel completeness definition of event logs and corresponding generation algorithm

A novel completeness definition of event logs and corresponding generation algorithm

Towards Comprehensive Support for Business Process Behavior Similarity Measure

TrazasBP: A Framework for Business Process Models Discovery Based on Execution Cases

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