Process Compliance Measurement Based on Behavioural Profiles

Weidlich, Matthias; Polyvyanyy, Artem; Desai, Narayan; Mendling, Jan

doi:10.1007/978-3-642-13094-6_38

Cited by 28 publications

(20 citation statements)

References 14 publications

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“…Many existing precision metrics (e.g., [25,30,34]) do not explicitly take into account possible deviations between the behavior observed in the event log with the behavior modeled in the models, while many case studies show that such deviations often occur in practice (e.g., [11,16,19,20,23,31,35,36]). Thus, these metrics might be biased due to unfitting logs and models.…”

Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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Conformance checking techniques compare observed behavior (i.e., event logs) with modeled behavior for a variety of reasons. For example, discrepancies between a normative process model and recorded behavior may point to fraud or inefficiencies. The resulting diagnostics can be used for auditing and compliance management. Conformance checking can also be used to judge a process model automatically discovered from an event log. Models discovered using different process discovery techniques need to be compared objectively. These examples illustrate just a few of the many use cases for aligning observed and modeled behavior. Thus far, most conformance checking techniques focused on replay fitness, i.e., the ability to reproduce the event log. However, it is easy to construct models that allow for lots of behavior (including the observed behavior) without being precise.In this paper, we propose a method to measure precision of process models, given their event logs by first aligning the logs to the models. This way, the measurement is not sensitive to non-fitting executions and more accurate values can be obtained for non-fitting logs. Furthermore, we introduce several variants of the technique to deal better with incomplete logs and reduce possible bias due to behavioral property of process models. The approach has been implemented in the ProM 6 framework and tested against both artificial and real-life cases. Experiments show that the approach is robust to noise and applicable to handle logs and models of real-life complexity.

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Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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show abstract

“…[4,7,9]) assume that the event log perfectly fits the model, while many case studies show that this assumption does not hold (e.g. [5,8,14]). In this paper, we do not use such assumptions and propose a robust approach to measure the precision between an event log and a model.…”

Section: Introductionmentioning

confidence: 99%

Alignment Based Precision Checking

Adriansyah

Muñoz-Gama

Carmona

et al. 2013

Business Process Management Workshops

108

100

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Abstract. Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today's information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.

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“…The formal definition of the behavioral profiles and the region behavioral profiles is given as follow: Definition 3 [7] (Behavioral Profiles (Process Model)) Let The set of the three relations is the behavioral profiles of PM . Definition 4 [8] (Behavioral Profiles (Log)) Let PM be a Petri net model, and L be a log of PM . A pair…”

Section: International Conference On Electronic Science and Automatiomentioning

confidence: 99%

A Method of Process Mining Based on Model Merged

Hua¹,

Fang²,

Liu³

2015

Proceedings of the 2015 International Conference on Electronic Science and Automation Control

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Abstract-In the area of Business process mining, there are many methods are available to discover process models from event logs. A new process mining method is proposed based on model merged. Firstly, sub-models are established respectively according to the behavioral profiles of each event log. Then finding out the maximum matched regions of the sub-models, merging the maximum matched regions and adding the rest of the transitions into the suitable place. Thus, the merged model is obtained. The method considers the maximum matched regions as a whole, greatly simplifying the process of modeling. Finally, a simple example is listed to show the feasibility of the method.

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Process Compliance Measurement Based on Behavioural Profiles

Cited by 28 publications

References 14 publications

Measuring precision of modeled behavior

Measuring precision of modeled behavior

Alignment Based Precision Checking

A Method of Process Mining Based on Model Merged

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