Auto-completion for diagram editors based on graph grammars

Mazanek, Steffen; Maier, Sonja; Minas, Mark

doi:10.1109/vlhcc.2008.4639094

Cited by 20 publications

(7 citation statements)

References 3 publications

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“…It should also be noted that there is a branch of related work that addresses modeling support for users [13,16,26,27,31]. This research, though not directly related to process verification, aims at providing systematic modeling support methods to users for developing process models.…”

Section: Discussion and Related Workmentioning

confidence: 99%

An integer programming based approach for verification and diagnosis of workflows

Eshuis

Kumar

2010

Data & Knowledge Engineering

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Workflow analysis is indispensable to capture modeling errors in workflow designs. While several workflow analysis approaches have been defined previously, these approaches do not give precise feedback, thus making it hard for a designer to pinpoint the exact cause of modeling errors. In this paper we introduce a novel approach for analyzing and diagnosing workflows based on integer programming (IP). Each workflow model is translated into a set of IP constraints. Faulty control flow connectors can be easily detected using the approach by relaxing the corresponding constraints. We have implemented this diagnosis approach in a tool called DiagFlow which reads and diagnoses XPDL models using an existing open source IP solver as a backend. We show that the diagnosis approach is correct and illustrate it with realistic examples. Moreover, the approach is flexible and can be extended to handle a variety of new constraints, as well as to support new workflow patterns. Results of testing on large process models show that DiagFlow outperforms a state of the art tool like Woflan in terms of the solution time.

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Section: Discussion and Related Workmentioning

confidence: 99%

An integer programming based approach for verification and diagnosis of workflows

Eshuis

Kumar

2010

Data & Knowledge Engineering

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“…Up to now, some proposals that lead to prototypical developments have been made in the area of recommendation-based process modeling [8] such as auto-completion approaches [9] [6] [10] [11] or auto-suggest features [12] [13] or recommendation methods for improving business process modeling [14]. However, these contributions rarely provide an explicit and detailed data model, so modelling the data perspective when building such tools has to start from scratch.…”

Section: Motivation and Relevancementioning

confidence: 99%

Data Model Development for Process Modeling Recommender Systems

Fellmann¹,

Metzger²,

Thomas³

2016

Lecture Notes in Business Information Processing

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Part 2: Regular PapersInternational audienceThe manual construction of business process models is a time-consuming and error-prone task. To ease the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used e.g. in e-commerce, such techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be addressed. In order to improve the situation, we develop a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). We systematically develop the model in a stepwise approach using established requirements and validate it against a data model that has been reverse-engineered from a real-world system. We expect that our contribution will provide a useful starting point for designing the data perspective of process modeling recommendation features

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“…This becomes crucial when recommending a number of di↵erent modeling activities consisting of similar editing operations. Mazanaek et al [11,12] studied auto-completions for model and diagram editors in general. Based on graph grammars, their approach calculates all possible completions for incomplete model graphs.…”

Section: Related Workmentioning

confidence: 99%

Recommending Auto-completions for Software Modeling Activities

Kuschke

Mäder

Rempel

2013

Lecture Notes in Computer Science

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Abstract. Auto-completion of textual inputs benefits software developers using IDEs and editors. However, graphical modeling tools used to design software do not provide this functionality. The challenges of recommending auto-completions for graphical modeling activities are largely unexplored. Recommending auto-completions during modeling requires detecting meaningful partly completed activities, tolerating variance in user actions, and determining the most relevant activity that a user wants to perform. This paper proposes an approach that works in the background while a developer is creating or evolving a model and handles all these challenges. Editing operations are analyzed and matched to a predefined but extensible catalog of common modeling activities for structural UML models. In this paper we solely focus on determining recommendations rather than automatically completing an activity. We demonstrated the quality of recommendations generated by our approach in a controlled experiment with 16 students evolving models. We recommended 88% of the activities that a user wanted to perform within a short list of ten recommendations.

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Auto-completion for diagram editors based on graph grammars

Cited by 20 publications

References 3 publications

An integer programming based approach for verification and diagnosis of workflows

An integer programming based approach for verification and diagnosis of workflows

Data Model Development for Process Modeling Recommender Systems

Recommending Auto-completions for Software Modeling Activities

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