[show abstract][hide abstract] ABSTRACT: Many organizations build up their business process management activities in an incremental way. As a result, there is no overarching structure defined at the beginning. However, as business In this paper, we introduce a technique for automatically extracting process categories from process model collections and test it using a collection from industry. The results demonstrate the usefulness of the technique by revealing issues of the pre-existing process categories. In this way, we contribute to the field of process model management and quality assurance.
Process Model Collection Workshops at the 11th Business Process Management Conference, Beijing, China; 08/2013
[show abstract][hide abstract] ABSTRACT: ContextThe Business Process Model and Notation (BPMN) standard informally defines a precise execution semantics. It defines how process instances should be updated in a model during execution. Existing formalizations of the standard are incomplete and rely on mappings to other languages.Objective
This paper provides a BPMN 2.0 semantics formalization that is more complete and intuitive than existing formalizations.Method
The formalization consists of in-place graph transformation rules that are documented visually using BPMN syntax. In-place transformations update models directly and do not require mappings to other languages. We have used a mature tool and test-suite to develop a reference implementation of all rules.ResultsOur formalization is a promising complement to the standard, in particular because all rules have been extensively verified and because conceptual validation is facilitated (the informal semantics also describes in-place updates).Conclusion
Since our formalization has already revealed problems with the standard and since the BPMN is still evolving, the maintainers of the standard can benefit from our results. Moreover, tool vendors can use our formalization and reference implementation for verifying conformance to the standard.
Information and Software Technology 02/2013; 55(2):365–394. · 1.52 Impact Factor
[show abstract][hide abstract] ABSTRACT: Nowadays, it is common for organizations to maintain collections of hundreds or even thousands of business processes. Techniques exist to search through such a collection, for business process models that are similar to a given query model. However, those techniques compare the query model to each model in the collection in terms of graph structure, which is inefficient and computationally complex. This paper presents an efficient algorithm for similarity search. The algorithm works by efficiently estimating model similarity, based on small characteristic model fragments, called features. The contribution of this paper is threefold. First, it presents three techniques to improve the efficiency of the currently fastest similarity search algorithm. Second, it presents a software architecture and prototype for a similarity search engine. Third, it presents an advanced evaluation of the algorithm. Experiments show that the algorithm in this paper helps to perform similarity search about 10 times faster than the original algorithm.
Distributed and Parallel Databases 02/2012; · 0.81 Impact Factor
[show abstract][hide abstract] ABSTRACT: Business process models are increasingly used by companies, often yielding repositories of several thousand models. These models are of great value for business analysis such as service identification or process standardization. A problem is though that many of these analyses require the pairwise comparison of process models, which is hardly feasible to do manually given an extensive number of models. While the computation of similarity between a pair of process models has been intensively studied in recent years, there is a notable gap on automatically matching activities of two process models. In this paper, we develop an approach based on semantic techniques and probabilistic optimization. We evaluate our approach using a sample of admission processes from different universities.
International Conference on Business Process Management; 01/2012
[show abstract][hide abstract] ABSTRACT: ContextLarge organizations often run hundreds or even thousands of different business processes. Managing such large collections of business process models is a challenging task. Software can assist in performing that task, by supporting common management functions such as storage, search and version management of models. It can also provide advanced functions that are specific for managing collections of process models, such as managing the consistency of public and private processes. Software that supports the management of large collections of business process models is called: business process model repository software.Objective
This paper contributes to the development of business process model repositories, by analyzing the state of the art.Method
To perform the analysis a literature survey and a comparison of existing (business process model) repository technology is performed.ResultThe results of the state of the art analysis are twofold. First, a framework for business process model repositories is presented, which consists of a management model and a reference architecture. The management model lists the functionality that can be provided and the reference architecture presents the components that provide that functionality. Second, an analysis is presented of the extent to which existing business process model repositories implement the functionality from the framework.Conclusion
The results presented in the paper are valuable as a comprehensive overview of business process model repository functionality. In addition they form a basis for a future research agenda. We conclude that existing repositories focus on traditional functionality rather than exploiting the full potential of information management tools, thus we show that there is a strong basis for further research.
Information & Software Technology. 01/2012; 54:380-395.
[show abstract][hide abstract] ABSTRACT: Business process models are becoming available in large numbers due to their popular use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: How can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionalities and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.
[show abstract][hide abstract] ABSTRACT: It is common for large and complex organizations to maintain repositories of business process models in order to document and to continuously improve their operations. Given such a repository, this paper deals with the problem of retrieving those process models in the repository that most closely resemble a given process model or fragment thereof. The paper presents three similarity metrics that can be used to answer such queries: (i) label matching similarity that compares the labels attached to process model elements; (ii) structural similarity that compares element labels as well as the topology of process models; and (iii) behavioral similarity that compares element labels as well as causal relations captured in the process model. These similarity metrics are experimentally evaluated in terms of precision and recall, and in terms of correlation of the metrics with respect to human judgement. The experimental results show that all three metrics yield comparable results, with structural similarity slightly outperforming the other two metrics. Also, all three metrics outperform traditional search engines when it comes to searching through a repository for similar business process models.
[show abstract][hide abstract] ABSTRACT: Modularization is a widely advocated mechanism to manage a business process model's size and complexity. However, the widespread use of subprocesses in models does not rest on solid evidence for its benefits to enhance their comprehension, nor are the criteria clear how to identify subprocesses. In this paper, we describe an empirical investigation to test the effectiveness of using subprocesses in real-life process models. Our results suggest that subprocesses may foster the understanding of a complex business process model by their “information hiding” quality. Furthermore, we explored different categories of criteria that can be used to automatically derive process fragments that seem suitable to capture as subprocesses. From this exploration, approaches that consider the connectedness of subprocesses seem most attractive to pursue. This insight can be used to develop tool support for the modularization of business process models.
[show abstract][hide abstract] ABSTRACT: ContextIn order to ensure high quality of a process model repository, refactoring operations can be applied to correct anti-patterns, such as overlap of process models, inconsistent labeling of activities and overly complex models. However, if a process model collection is created and maintained by different people over a longer period of time, manual detection of such refactoring opportunities becomes difficult, simply due to the number of processes in the repository. Consequently, there is a need for techniques to detect refactoring opportunities automatically.
Information & Software Technology. 01/2011; 53:937-948.
[show abstract][hide abstract] ABSTRACT: Nowadays, business process management plays an important role in the management of organizations. More and more organizations
describe their operations as business processes, and the intra- and inter-organizational interactions between operations as
services. It is common for organizations to have collections of hundreds or even thousands of business processes. Consequently,
techniques are required to quickly find relevant business process models in such a collection. Currently, techniques exist
that can rank all business process models in a collection based on their similarity to a query business process model. However,
those techniques compare the query model with each model in the collection in terms of graph structure, which is inefficient
and computationally complex. Therefore, this paper presents a technique to make this more efficient. The technique selects
small characteristic model fragments, called features, which are used to efficiently estimate model similarities and classify
them as relevant, irrelevant or potentially relevant to a query model. Only potentially relevant models must be compared using the existing techniques. Experiments show that this helps to retrieve similar models at least
3.5 times faster without impacting the quality of the results; and 5.5 times faster if a quality reduction of 1% is acceptable.
[show abstract][hide abstract] ABSTRACT: Compatibility of two process models can be veried using com- mon notions of behaviour inheritance. However, these notions postulate 1:1 correspondences between activities of both models. This assumption is violated once activities from one model are rened or collapsed in the other model or in case there are groups of corresponding activities. Therefore, our work lifts the work on behaviour inheritance to the level of complex 1:n and n:m correspondences. Our contribution is (1) the deni- tion of notions of behaviour compatibility for models that have complex correspondences and (2) a structural characterisation of these notions for sound free-choice process models that allows for computationally ecient reasoning. We show the applicability of our technique, by applying it in a case study in which we determine the compatibility between a set of reference process models and models that implement them.
Business Process Management - 8th International Conference, BPM 2010, Hoboken, NJ, USA, September 13-16, 2010. Proceedings; 01/2010
[show abstract][hide abstract] ABSTRACT: Business process models can be compared, for example, to determine their consistency. Any comparison between process models
relies on a mapping that identifies which activity in one model corresponds to which activity in another. Tools that generate
such mappings are called matchers. This paper presents the ICoP framework, which can be used to develop such matchers. It
consists of an architecture and re-usable matcher components. The framework enables the creation of matchers from the re-usable
components and, if desired, newly developed components. It focuses on matchers that also detect complex correspondences between
groups of activities, where existing matchers focus on 1:1 correspondences. We evaluate the framework by applying it to find
matches in process models from practice. We show that the framework can be used to develop matchers in a flexible and adaptable
manner and that the resulting matchers can identify a significant number of complex correspondences.
Advanced Information Systems Engineering, 22nd International Conference, CAiSE 2010, Hammamet, Tunisia, June 7-9, 2010. Proceedings; 01/2010
[show abstract][hide abstract] ABSTRACT: As business process management is increasingly applied in practice, more companies document their operations in the form of
process models. Since users require descriptions of one process on various levels of detail, there are often multiple models
created for the same process. Business process model abstraction emerged as a technique reducing the number of models to be
stored: given a detailed process model, business process model abstraction delivers abstract representations for the same
process. A key problem in many abstraction scenarios is the transition from detailed activities in the initial model to coarse-grained
activities in the abstract model. This transition is realized by an aggregation operation clustering multiple activities to
a single one. So far, humans decide on how to aggregate, which is expensive. This paper presents a semi-automated approach
to activity aggregation that reduces the human effort significantly. The approach takes advantage of an activity meronymy
relation, i.e., part-of relation defined between activities. The approach is semi-automated, as it proposes sets of meaningful
aggregations, while the user still decides. The approach is evaluated by a real-world use case.
Conceptual Modeling - ER 2010, 29th International Conference on Conceptual Modeling, Vancouver, BC, Canada, November 1-4, 2010. Proceedings; 01/2010