Multi-phase Process Mining: Building Instance Graphs

Dongen, van Bf Boudewijn; Aalst, van der Wmp Wil

doi:10.1007/978-3-540-30464-7_29

Cited by 128 publications

(100 citation statements)

References 13 publications

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“…Since the mid-nineties several groups have been working on techniques for process mining [7,4,8,12,16,20,21,40], i.e., discovering process models based on observed events. In [6] an overview is given of the early work in this domain.…”

Section: Related Workmentioning

confidence: 99%

“…However, this simple algorithm has problems dealing with complicated routing constructs and noise (like most of the other approaches described in literature). In [20,21] a more robust but less precise approach is presented.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities [7,8,12,16,20,21,40]. The basic idea of control-flow discovery is very simple: given an event log containing a set of traces, automatically construct a suitable process model "describing the behavior" seen in the log.…”

Section: Introductionmentioning

confidence: 99%

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Process mining: a two-step approach to balance between underfitting and overfitting

et al. 2008

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Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such "overfitting" by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are "overfitting" (allow only for what has actually been observed) while other parts may be "underfitting" (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between "overfitting" and "underfitting". To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the "theory of regions", the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Process mining: a two-step approach to balance between underfitting and overfitting

et al. 2008

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“…One of the first attempts in this area is multi-phase process mining [15,16]. Initially generating a process model for each individual process instance/case, these instance graphs are stepwise aggregated based on their similarity to introduce more general models.…”

Section: Related Workmentioning

confidence: 99%

Discovering Workflow Changes with Time-Based Trace Clustering

Accorsi

Stocker

2012

Lecture Notes in Business Information Processing

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Abstract. This paper proposes a trace clustering approach to support process discovery of configurable, evolving process models. The clustering approach allows auditors to distinguish between different process variants within a timeframe, thereby visualizing the process evolution. The main insight to cluster entries is the "distance" between activities, i.e. the number of steps between an activity pair. By observing non-transient modifications on the distance, changes in the original process shape can be inferred and the entries clustered accordingly. The paper presents the corresponding algorithms and exemplifies its usage in a running example.

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“…Some techniques generate dependency graphs [15,16], others use probabilistic models [17,18], and most of the current techniques are geared towards retrieving Petri net models [19]. To communicate with end users, it may be necessary to translate Petri nets into other kinds of models, including business process notations such as EPC 6 [20,21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Process Control Flow from I/O Operations

Diniz

Ferreira

2008

Lecture Notes in Computer Science

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Abstract. Many end users will expect the output of process mining to be a model they can easily understand. On the other hand, knowing which objects were accessed in each operation can be a valuable input for process discovery. From these two trends it is possible to establish an analogy between process mining and the discovery of program structure. In this paper we present an approach for extracting process controlflow from a trace of read and write operations over a set of objects. The approach is divided in two independent phases. In the first phase, Fourier analysis is used to identify periodic behavior that can be represented with loop constructs. In the second phase, a match-and-merge technique is used to produce a control-flow graph capable of generating the input trace and thus representing the process that generated it. The combination of these techniques provides a structured and compact representation of the unknown process, with very good results in terms of conformance metrics.

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Multi-phase Process Mining: Building Instance Graphs

Cited by 128 publications

References 13 publications

Process mining: a two-step approach to balance between underfitting and overfitting

Process mining: a two-step approach to balance between underfitting and overfitting

Discovering Workflow Changes with Time-Based Trace Clustering

Automatic Extraction of Process Control Flow from I/O Operations

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