Simplifying Process Model Abstraction: Techniques for Generating Model Names (Extended Abstract)

EMISA FORUM ◽  
2014 ◽  
Vol 34 (1) ◽  
pp. 37-37
Author(s):  
Henrik Leopold ◽  
Jan Mendling ◽  
Hajo A. Reijers ◽  
Marcello La Rosa
Keyword(s):  
Process Model ◽  
Model Abstraction

Statistical Verification of Process Model Conformance to Execution Log Considering Model Abstraction

2018 ◽  
Vol 27 (02) ◽  
pp. 1850002
Author(s):  
Sung-Hyun Sim ◽  
Hyerim Bae ◽  
Yulim Choi ◽  
Ling Liu
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Statistical Significance ◽  
Conformance Checking ◽  
Model Abstraction ◽  
Verification Method ◽  
Statistical Verification ◽  
New Concepts ◽  
Kolmogorov Smirnov ◽  
Process Execution

In Big data and IoT environments, process execution generates huge-sized data some of which is subsequently obtained by sensors. The main issue in such areas has been the necessity of analyzing data in order to suggest enhancements to processes. In this regard, evaluation of process model conformance to the execution log is of great importance. For this purpose, previous reports on process mining approaches have advocated conformance checking by fitness measure, which is a process that uses token replay and node-arc relations based on Petri net. However, fitness measure so far has not considered statistical significance, but just offers a numeric ratio. We herein propose a statistical verification method based on the Kolmogorov–Smirnov (K–S) test to judge whether two different log datasets follow the same process model. Our method can be easily extended to determinations that process execution actually follows a process model, by playing out the model and generating event log data from it. Additionally, in order to solve the problem of the trade-off between model abstraction and process conformance, we also propose the new concepts of Confidence Interval of Abstraction Value (CIAV) and Maximum Confidence Abstraction Value (MCAV). We showed that our method can be applied to any process mining algorithm (e.g. heuristic mining, fuzzy mining) that has parameters related to model abstraction. We expect that our method will come to be widely utilized in many applications dealing with business process enhancement involving process-model and execution-log analyses.

Business Process Model Abstraction Based on Fuzzy Clustering Analysis

2019 ◽  
Vol 28 (03) ◽  
pp. 1950007
Author(s):  
Nan Wang ◽  
Shanwu Sun ◽  
Ying Liu ◽  
Senyue Zhang
Keyword(s):  
Business Process ◽  
Fuzzy Clustering ◽  
Process Model ◽  
Control Flow ◽  
Business Process Model ◽  
Model Abstraction ◽  
Fuzzy Matrix ◽  
Fuzzy Clustering Analysis ◽  
Complex Process ◽  
Hard Clustering

The most prominent Business Process Model Abstraction (BPMA) use case is a construction of a process “quick view” for rapidly comprehending a complex process. Researchers propose various process abstraction methods to aggregate the activities most of which are based on [Formula: see text]-means hard clustering. This paper focuses on the limitation of hard clustering, i.e. it cannot identify the special activities (called “edge activities” in this paper) and each activity must be classified to some subprocess. A new method is proposed to classify activities based on fuzzy clustering which generates a fuzzy matrix by computing the possibilities of activities belonging to subprocesses. According to this matrix, the “edge activities” can be located. Considering the structure correlation feature of the activities in subprocesses, an approach is provided to generate the initial clusters based on the close connection characteristics of subprocesses. A hard partition algorithm is proposed to classify the edge activities and it evaluates the generated abstract models according to a new index designed by control flow order preserving requirement and the evaluation results guide the edge activities to be classified to the optimal hard partition. The proposed method is applied to a process model repository in use. The results verify the validity of the measurement based on the virtual document to generating fuzzy matrix. Also it mines the threshold parameter in the real world process model collection enriched with human designed subprocesses to compute the fuzzy matrix. Furthermore, a comparison is made between the proposed method and the [Formula: see text]-means clustering and the results show our approach more closely approximating the decisions of the involved modelers to cluster activities and it contributes to the development of modeling support for effective process model abstraction.

scholarly journals Business Process Model Abstraction

2010 ◽  
pp. 149-166 ◽  
Author(s):  
Artem Polyvyanyy ◽  
Sergey Smirnov ◽  
Mathias Weske
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Process Model ◽  
Model Abstraction

scholarly journals Business Process Model Abstraction

2014 ◽  
pp. 147-165 ◽  
Author(s):  
Artem Polyvyanyy ◽  
Sergey Smirnov ◽  
Mathias Weske
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Process Model ◽  
Model Abstraction

BUSINESS PROCESS MODEL ABSTRACTION BASED ON SYNTHESIS FROM WELL-STRUCTURED BEHAVIORAL PROFILES

2012 ◽  
Vol 21 (01) ◽  
pp. 55-83 ◽  
Author(s):  
SERGEY SMIRNOV ◽  
MATTHIAS WEIDLICH ◽  
JAN MENDLING
Keyword(s):  
Business Process ◽  
Process Model ◽  
Control Flow ◽  
Process Models ◽  
Coarse Grained ◽  
Business Process Model ◽  
Model Abstraction ◽  
Behavioral Profiles ◽  
Level Model ◽  
High Level

There are several motives for creating process models ranging from technical scenarios in workflow automation to business scenarios in which management decisions are taken. As a consequence, companies typically have different process models for the same process, which differ in terms of granularity. In this context, business process model abstraction serves as a technique that takes a process model as an input and derives a high-level model with coarse-grained activities and the corresponding control flow between them. In this way, business process model abstraction reduces the number of models capturing the same business process on different abstraction levels. In this article, we provide a solution to the problem of deriving the control flow of an abstract process model for the case that an arbitrary grouping of activities is permitted. To this end, we use behavioral profiles and prove that the soundness of the synthesized process model requires a notion of well-structuredness of the abstract model behavioral profile. Furthermore, we demonstrate that the activities can be grouped according to the data flow of the model in a meaningful way, and that this grouping does not directly coincides with a structural decomposition of the process, which is generally assumed by other abstraction approaches. This finding emphasizes the need for handling arbitrary activity groupings in business process model abstraction.

scholarly journals Simplifying process model abstraction: Techniques for generating model names

2014 ◽  
Vol 39 ◽  
pp. 134-151 ◽  
Author(s):  
Henrik Leopold ◽  
Jan Mendling ◽  
Hajo A. Reijers ◽  
Marcello La Rosa
Keyword(s):  
Process Model ◽  
Model Abstraction
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