Making sense of business process descriptions: An experimental comparison of graphical and textual notations

Ottensooser, Avner; Fekete, Alan; Reijers, Hajo A.; Mendling, Jan; Menictas, Con

doi:10.1016/j.jss.2011.09.023

Cited by 108 publications

(76 citation statements)

References 61 publications

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“…Our results show that for all tasks, diagrams were rated most preferred, and structured text was consistently preferred over text. These results are in line with related studies [10,11,38] and may reflect a general level of awareness of advantages of these representations in terms of elimination of irrelevant information and reduction of cognitive effort [37,82,83], in particular when designing instructions [11] -such as instructions for carrying out work tasks in a process.…”

Section: Summary Of Findingssupporting

confidence: 90%

“…[36] showed that diagrammatic flowchart representations were equally effective for most programming related tasks as constrained, structured text, while text was less efficient. Prior research in general has suggested that these visual cues can aid cognitive processing better than textual representations [37], as long as the graphical representation in itself is not too complex to comprehend [38]. The grammars most frequently in use [3] mostly fall into this category.…”

Section: Diagram Representationsmentioning

confidence: 99%

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Exploring cognitive style and task-specific preferences for process representations

Figl

Recker

2014

Requirements Eng

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Process models describe someone's understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.

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Section: Summary Of Findingssupporting

confidence: 90%

Section: Diagram Representationsmentioning

confidence: 99%

Exploring cognitive style and task-specific preferences for process representations

Figl

Recker

2014

Requirements Eng

View full text Add to dashboard Cite

show abstract

“…The work in [34] demonstrates the impact the natural language in activity labels has on model comprehension. In general, most authors agree on the fact that text plus diagram provides better comprehension than any of the two in isolation [35,36,37]. Hence, the approach presented in this paper builds on these insights as it tries to lower the overall burden of process model comprehension.…”

Section: Related Workmentioning

confidence: 88%

Creating and Updating Personalized and Verbalized Business Process Descriptions

Kolb¹,

Leopold

Mendling

et al. 2013

Lecture Notes in Business Information Processing

Self Cite

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Abstract. The increasing adoption of process-aware information systems (PAISs) has resulted in large process model collections. To support users having different perspectives on complex processes and related data, a PAIS should enable personalized process views, i.e., user-specific abstractions of process models. Despite the abstraction achieved through views of the graphical process models, many end users still struggle with understanding these graphical models and their details. For selected user groups, therefore, a PAIS should provide verbalized process descriptions describing their role in the process. Existing PAISs neither provide mechanisms for managing process views nor verbalized process descriptions. While process views have been used as visual abstractions for large process models, so far no work exists on how to provide both personalized and verbalized process descriptions based on respective views. This paper presents an approach for creating such personalized and verbalized process descriptions based on process views. Furthermore, textual changes of a personalized and verbalized process description are correctly mapped to corresponding updates of the underlying process model. In this context, all other views and process descriptions related to this process model are migrated to the new version of the process model as well. Overall, our approach enables end users to understand and evolve large process models based on personalized and verbalized process descriptions.

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“…Also, editors and programming environments are easier to develop and less resource hungry for textual languages. A recent study of Ottensooser et al, [40] showed that complex processes and dependencies are more efficient to express in a textual syntax than a graphical one. For these reasons we decided to first implement a textual modeling language.…”

Section: Modeling Languagementioning

confidence: 99%

The next evolution of MDE: a seamless integration of machine learning into domain modeling

Hartmann

Moawad²,

Fouquet

et al. 2017

Softw Syst Model

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Abstract-Machine learning algorithms are designed to resolve unknown behaviours by extracting commonalities over massive datasets. Unfortunately, learning such global behaviours can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions, such systems have to continuously refine the behaviour on a per-element basis and compose these small learning units together. However, combining and composing learned behaviours from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviours and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as micro learning units. These micro learning units are modeled together with and at the same level as the domain data. We show, based on a smart grid case study, that our approach can be significantly more accurate than learning a global behaviour while the performance is fast enough to be used for live learning.

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Making sense of business process descriptions: An experimental comparison of graphical and textual notations

Cited by 108 publications

References 61 publications

Exploring cognitive style and task-specific preferences for process representations

Exploring cognitive style and task-specific preferences for process representations

Creating and Updating Personalized and Verbalized Business Process Descriptions

The next evolution of MDE: a seamless integration of machine learning into domain modeling

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