Recommendation Based Process Modeling Support: Method and User Experience

Hornung, Thomas; Koschmider, Agnes; Lausen, Georg

doi:10.1007/978-3-540-87877-3_20

Cited by 50 publications

(33 citation statements)

References 11 publications

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“…To this end, similar models in a process model repository might be proposed as extensions to the currently modeled process using search techniques [16]. While this approach also builds on a match of actions, business objects, and textual content, we believe that action patterns are more flexible, as they do not require the knowledge about an exact continuation of a process.…”

Section: Related Workmentioning

confidence: 99%

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Object-Sensitive Action Patterns in Process Model Repositories

Smirnov

Weidlich

Mendling

et al. 2011

Business Process Management Workshops

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Abstract. Organizations maintain large repositories of business process models. While maintenance and management of these repositories are challenging, they also offer opportunities when used as a knowledge base systematically. For instance, repositories can be leveraged to provide modeling support and, therefore, help to assure the consistency of newly created models with the existing ones. In the previous work we have introduced action patterns as reusable blocks of process models that can be derived from a model repository. In this paper we advance the initial results interpreting the action concept as a composition of a verb and a business object. The subsequently identified action pattern types allow for fine-grained modeling support. We evaluate the novel concepts and compare them to the established action patterns using as a benchmark the SAP Reference Model, the real world process model collection.

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Section: Related Workmentioning

confidence: 99%

“…Textual labels are used for matching and comparing process models [16,22]. Recent works by Becker et al reuse parsing techniques from computer linguistics to identify the various parts of an activity label [23].…”

Section: Related Workmentioning

confidence: 99%

Object-Sensitive Action Patterns in Process Model Repositories

Smirnov

Weidlich

Mendling

et al. 2011

Business Process Management Workshops

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“…Finally, Ehrig et al [13] match task labels based on structural and semantic properties, among other options using WordNet synonyms [45]. While this technique is close to our semantic matching technique, it has not been validated, but applied as a tool for recommendation support [46].…”

Section: Related Workmentioning

confidence: 99%

Similarity of business process models: Metrics and evaluation

Dijkman

Dumas

Dongen

et al. 2011

Information Systems

488

303

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a b s t r a c tIt is common for large 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 models in the repository that most closely resemble a given process model or fragment thereof. Up to now, there is a notable research gap on comparing different approaches to this problem and on evaluating them in the same setting. Therefore, this paper presents three similarity metrics that can be used to answer queries on process repositories: (i) node matching similarity that compares the labels and attributes 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 metrics are experimentally evaluated in terms of precision and recall. The results show that all three metrics yield comparable results, with structural similarity slightly outperforming the other two metrics. Also, all three metrics outperform text-based search engines when it comes to searching through a repository for similar business process models.

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“…For instance, Smirnov et al [12] provide so-called co-occurrence action patterns in response to action/task specifications by the user; recommendations are provided based on label similarity, and also come with the necessary control flow logic to connect the suggested action. Hornung et al [8] provide users with a keyword search facility that allows them to retrieve process models whose labels are related to the provided keywords; the algorithm applies the traditional TF-IDF technique from information retrieval to process models, turning the repository of process models into a keyword vector space. Gschwind et al [7] allow users to use the control flow patterns introduced by Van der Aalst et al [14], just like other modeling elements.…”

Section: Related Workmentioning

confidence: 99%

“…For the sub-graph mining we can choose among the state of the art sub-graph mining algorithms [13]. Then, we get from the mashup repository the list of mashup fragments that match the frequent sub-graphs mined in the previous step (lines [6][7][8][9][10][11]. We do this, so that next we can mine both the parameter value and data mapping patterns using again standard itemset mining algorithms (lines 13-21).…”

Section: Mining Algorithmsmentioning

confidence: 99%

Assisted Mashup Development: On the Discovery and Recommendation of Mashup Composition Knowledge

Rodríguez

Chowdhury

Daniel

et al. 2013

Web Services Foundations

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Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes' web-based mashup editor.

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Recommendation Based Process Modeling Support: Method and User Experience

Cited by 50 publications

References 11 publications

Object-Sensitive Action Patterns in Process Model Repositories

Object-Sensitive Action Patterns in Process Model Repositories

Similarity of business process models: Metrics and evaluation

Assisted Mashup Development: On the Discovery and Recommendation of Mashup Composition Knowledge

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