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Experimental And Quasi Experimental Designs For Research

Social Network Analysis: Computer Programs

Text-based and model-based process descriptions have their own particular strengths and, as such, appeal to different stakeholders. For this reason, it is not unusual to find within an organization descriptions of the same business processes in both modes. When considering that hundreds of such descriptions may be in use in a particular organization by dozens of people, using a variety of editors, there is a clear risk that such models become misaligned. To reduce the time and effort needed to repair such situations, this paper presents the first approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach leverages natural language processing techniques to identify cases where the two process representations describe activities in different orders, as well as model activities that are missing from the textual description. A quantitative evaluation with 46 real-life model-text pairs demonstrates that our approach allows users to quickly and effectively identify those descriptions in a process repository that are inconsistent.

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Detecting Inconsistencies Between Process Models and Textual Descriptions

Business process models play an important role in today’s enterprises, hence, model repositories may contain hundreds of models. These models are, for example, reused during process modeling activities or utilized to check the conformance of processes with legal regulations. With respect to the amount of models, such applications benefit from or even require detailed insights into the correspondences between process models or between process models’ nodes. Therefore, various process similarity and matching measures have been proposed during the past few years. This article provides an overview of the state-of-the-art regarding business process model similarity measures and aims at analyzing which similarity measures exist, how they are characterized, and what kind of calculations are typically applied to determine similarity values. Finally, the analysis of 123 similarity measures results in the suggestions to conduct further comparative analyses of similarity measures, to investigate the integration of human input into similarity measurement, and to further analyze the requirements of similarity measurement usage scenarios as future research opportunities.

Similarity of Business Process Models—A State-of-the-Art Analysis

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

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Matching events and activities by integrating behavioral aspects and label analysis

Process model matching refers to the automatic identification of correspondences between the activities of process models. Application scenarios of process model matching reach from model validation over harmonization of process variants to effective management of process model collections. Recognizing this, several process model matching techniques have been developed in recent years. However, to learn about specific strengths and weaknesses of these techniques, a common evaluation basis is indispensable. The second edition of the Process Model Matching Contest in 2015 hence addresses the need for effective evaluation by defining process model matching problems over published data sets. This paper summarizes the setup and the results of the contest. Next to a description of the contest matching problems, the paper provides short descriptions of all matching techniques that have been submitted for participation. In addition, we present and discuss the evaluation results and outline directions for future work in the field of process model matching.

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The Process Model Matching Contest 2015

Matching business process models and their node labels plays an important role for business process management. Many matching algorithms using natural language processing (NLP) techniques exist but do not exploit the opportunities of machine learning though it is generally agreed that a learning approach has great potential in the field of NLP. Therefore, we develop a matching approach based on supervised learning using a language-driven similarity function in order to reproduce a human judgement. Additionally, we implement and evaluate our approach using established quality measures, consisting of precision, recall and F-measure. We conduct an evaluation based on real world process models that demonstrates the potential and the limitations of our machine learning approach. The results show a significant learning effect for matching unknown models without predefined rules. The matching quality is comparable to existing matchers. However, the matching quality seems to depend on the one hand on the available training data and on the other hand on the complexity of the chosen similarity function. Further research efforts have to be undertaken in order to improve our approach. This will include developing a more elaborate similarity function containing more linguistic characteristics of a label as well as integrating contextual information.

An approach for semantic business process model matching using supervised machine learning

Organizations nowadays possess huge repositories of process models. Inductive reference modelling can save costs and time by reusing process parts of process models belonging to a common domain. The inductive development of a reference model for a large corpus of process models is a difficult problem. Quite a few, primarily heuristic approaches have been proposed to the research community that require an approximate matching between the single processes. With our approach, we introduce a new concept that brings in for the first time an abstract efficiency simulation of the social collaboration around knowledge-based process models. A reference model is assembled featuring at least the topological minimum requirements to be significantly more efficient than the input process models. Our evaluation indicates that the approach is able to generate reference process models that are more efficient than the input process models and at least as a reference model designed by an expert.

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Inductive Reference Modelling Based on Simulated Social Collaboration

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The IWi process model corpus

Business process models (PMs) model activities along the execution of business processes. A success factor of process activities is collaboration providing the major part of implicit process knowledge. Applying social network analysis (SNA) on PMs can help understanding the formation of successful organizational structures around PMs. In that context we want to investigate, how the topology of process participants (performers in the following) can be formalized respectively optimized and what are drivers for the efficient interaction between performers and PM. Our approach is to simulate multiple performer networks (PNs) for a corpus containing 9 university admission PMs. Different Social network generators are used in order to generate realistic and extremely structured organizations. The performers in the PN are given random capabilities and according to them, they are associated to the PM functions. For each generated PN, its efficiency is measured by the number of iterations the performers need to finish their tasks at the process functions along the process flow. We describe the topological patterns of the most efficient PNs as we assume them to be success determinants for collaborative business processes. We found optimal parameters to generate efficient PNs for the given corpus. The best efficiency is reached by teams with on average 5 global connected colleagues. This number has the greatest influence on the average PM performance. We also found too many (global) connections between performers to decrease their efficiency and that teams need hierarchy to be efficient in a process.

Andreas Sonntag

Papers

The Process Model Matching Contest 2015

An approach for semantic business process model matching using supervised machine learning

Inductive Reference Modelling Based on Simulated Social Collaboration

The IWi process model corpus

Efficiency of Generated Performer Networks in Collaborative Business Process Models