Generating Web Services for Statistical Survey Packages from Domain-specific Visual Languages

In the last years, researchers are exploring the feasibility of visual language editors in domain-specific domains where their alleged user-friendliness can be exploited to involve end-users in configuring their artifacts. In this chapter, the authors present an experimental user study conducted to validate the hypothesis that adopting a visual language could help prospective end-users of an electronic medical record define their own document-related local rules. This study allows them to claim that their visual rule editor based on the OpenBlocks framework can be used with no particular training as proficiently as with specific training, and it was found user-friendly by the user panel involved. Although the conclusions of this study cannot be broadly generalized, the findings are a preliminary contribution to show the importance of visual languages in domain-specific rule definition by end-users with no particular IT skills, like medical doctors are supposed to represent.

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Integrating Measures and Redesigns in the Definition of Domain Specific Visual Languages

Model-Driven Software Development ◽

10.4018/978-1-60566-006-6.ch006 ◽

2009 ◽

pp. 119-146

Author(s):

Esther Guerra ◽

Juan de Lara ◽

Paloma Díaz

Keyword(s):

Graph Transformation ◽

Success Factor ◽

Visual Languages ◽

Model Quality ◽

Web Engineering ◽

Product Metrics ◽

Model Driven ◽

Domain Specific ◽

Modelling Environment ◽

Definition Of

The goal of this work is to facilitate the task of integrating measurement and redesign tools in modelling environments for Domain Specific Visual Languages (DSVLs), reducing or eliminating the necessity of coding. With this purpose, we have created a DSVL called SLAMMER that includes generalizations of some of the more used types of product metrics and frequent model manipulations, which can be easily customised for any other DSVL in a graphical way. The metric customisation process relies on visual patterns for the specification of the elements that should be measured in each metric type, while redesigns (as well as other actions) can be specified either personalizing generic templates or by means of graph transformation systems. The provided DSVL also allows creating new metrics, composing metrics, and executing actions guided by measurement values. The approach has been empirically validated by its implementation in a meta-modelling tool, which has been used for several DSVLs. In this way, together with the DSVL specification, a SLAMMER model can be provided containing a suite of metrics and actions that will become available in the final modelling environment. In this chapter we show a case study for a notation in the web engineering domain. As ensuring model quality is a key success factor in many computer science areas, even crucial in model-driven development, we believe that the results of this work benefit all of them by providing automatic support for the specification, generation and integration of measurement and redesign tools with modelling environments.

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Special Issue on ‘Visual Languages for End-User and Domain-Specific Programmingʼ

Journal of Visual Languages & Computing ◽

10.1016/s1045-926x(99)90137-4 ◽

1999 ◽

Vol 10 (5) ◽

pp. 561

Keyword(s):

Special Issue ◽

End User ◽

Visual Languages ◽

Domain Specific

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Context-Aware Web Service Clustering and Visualization

International Journal of Web Services Research ◽

10.4018/ijwsr.2020100103 ◽

2020 ◽

Vol 17 (4) ◽

pp. 32-54

Author(s):

Banage T. G. S. Kumara ◽

Incheon Paik ◽

Yuichi Yaguchi

Keyword(s):

Web Services ◽

Web Service ◽

Service Discovery ◽

Search Space ◽

Context Aware ◽

Specific Context ◽

Web Service Discovery ◽

Domain Specific ◽

Clustering Approach ◽

Service Clustering

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.

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