Discovering Block-Structured Process Models from Incomplete Event Logs

Leemans, Sander J. J.; Fahland, Dirk; Aalst, Wil M. P. van der

doi:10.1007/978-3-319-07734-5_6

Cited by 122 publications

(119 citation statements)

References 39 publications

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“…For an incomplete log, a prime component can occur. [17] propose to use probabilistic activity relations in the case of an incomplete log. The cut with the highest probability is chosen.…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Optimized Process Models from Declarative Specifications

Mrasek

Mülle

Böhm

2015

Advanced Information Systems Engineering

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Abstract. Process models often are generic, i. e., describe similar cases or contexts. For instance, a process model for commissioning can cover both vehicles with an automatic and with a manual transmission, by executing alternative tasks. A generic process model is not optimal compared to one tailored to a specific context. Given a declarative specification of the constraints and a specific context, we study how to automatically generate a good process model and propose a novel approach. We focus on the restricted case that there are not any repetitions of a task, as is the case in commissioning and elsewhere, e. g., manufacturing. Our approach uses a probabilistic search to find a good process model according to quality criteria. It can handle complex real-world specifications containing several hundred constraints and more than one hundred tasks. The process models generated with our scheme are superior (nearly twice as fast) to ones designed by professional modelers by hand.

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“…For an incomplete log, a prime component can occur. [17] propose to use probabilistic activity relations in the case of an incomplete log. The cut with the highest probability is chosen.…”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Optimized Process Models from Declarative Specifications

Mrasek

Mülle

Böhm

2015

Advanced Information Systems Engineering

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“…The inductive miner (IM) [21], aims to discover blockstructured process models that are sound and fitting to the behavior represented in the event log. IM is an example of an algorithm that discovers process trees and for which rediscoverability has been proven.…”

Section: Related Workmentioning

confidence: 99%

“…A process tree is a compact abstract representation of a block-structured workflow net: a rooted tree in which leaves are labelled with activities and all other nodes are labelled with operators [21]. A process model is block-structured if splits and joins are always paired into Single-Entry-Single-Exit (SESE) fragments [25].…”

Section: Parallel Processes and Block-structured Parallel Process Modelsmentioning

confidence: 99%

Discovering Block–Structured Parallel Process Models from Causally Complete Event Logs

Lekić

Milicev

2016

Journal of Electrical Engineering

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α -algorithm is suitable to discover a large class of workflow (WF) nets based on the behaviour recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of discovering business process models based on examples of traces, ie, logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering technique, including the α -algorithm. We have also introduced the notion of causally complete logs, from which our modified algorithm can produce the same result as the α -algorithm from complete logs. The effect of these modifications on the efficiency of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. The application of the modified method for discovering block-structured models of parallel business processes is presented in this paper.K e y w o r d s: process mining, business process model discovery, block-structured parallel process models, complete log, α -algorithm

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“…Moreover, completely different approaches have been proposed, e.g., the different types of genetic process mining [14,15], techniques based on state-based regions [16,17], and techniques based on language-based regions [18,19]. Another, more recent, approach is inductive process mining where the event log is split recursively [20]. The latter technique always produces a block-structured and sound process model.…”

Section: Related Workmentioning

confidence: 99%

“…+ -+ --Leemans M., Episode discovery + -n.a. + -+ -+ Van der Aalst, α-algorithm [10] + + -+ + + --Weijters, Heuristics mining [11] + + -+ + + --De Medeiros, Genetic mining [14,15] + + -+ + + + + Solé, State Regions [16,17] + + -+ + + --Bergenthum, Language Regions [18,19] + + -+ + + --Leemans S.J.J., Inductive [20] + + + + + + + - Table 1. Feature comparison of discussed discovery algorithms…”

Section: Related Workmentioning

confidence: 99%

Discovery of Frequent Episodes in Event Logs

Leemans

Aalst

2015

Lecture Notes in Business Information Processing

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Abstract. Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

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Discovering Block-Structured Process Models from Incomplete Event Logs

Cited by 122 publications

References 39 publications

Automatic Generation of Optimized Process Models from Declarative Specifications

Automatic Generation of Optimized Process Models from Declarative Specifications

Discovering Block–Structured Parallel Process Models from Causally Complete Event Logs

Discovery of Frequent Episodes in Event Logs

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