Recommendation and weaving of reusable mashup model patterns for assisted development

With this article, we give an answer to one of the open problems of mashup development that users may face when operating a model-driven mashup tool, namely the lack of modeling expertise. Although commonly considered simple applications, mashups can also be complex software artifacts depending on the number and types of Web resources (the components) they integrate. Mashup tools have undoubtedly simplified mashup development, yet the problem is still generally nontrivial and requires intimate knowledge of the components provided by the mashup tool, its underlying mashup paradigm, and of how to apply such to the integration of the components. This knowledge is generally neither intuitive nor standardized across different mashup tools and the consequent lack of modeling expertise affects both skilled programmers and end-user programmers alike. In this article, we show how to effectively assist the users of mashup tools with contextual, interactive recommendations of composition knowledge in the form of reusable mashup model patterns. We design and study three different recommendation algorithms and describe a pattern weaving approach for the one-click reuse of composition knowledge. We report on the implementation of three pattern recommender plugins for different mashup tools and demonstrate via user studies that recommending and weaving contextual mashup model patterns significantly reduces development times in all three cases

Supporting End-User Development through a New Composition Model: An Empirical Study

End-user development (EUD) is much hyped, and its impact has outstripped even the most optimistic forecasts. Even so, the vision of end users programming their own solutions has not yet materialized. This will continue to be so unless we in both industry and the research community set ourselves the ambitious challenge of devising end to end an end-user application development model for developing a new age of EUD tools. We have embarked on this venture, and this paper presents the main insights and outcomes of our research and development efforts as part of a number of successful EU research projects. Our proposal not only aims to reshape software engineering to meet the needs of EUD but also to refashion its components as solution building blocks instead of programs and software developments. This way, end users will really be empowered to build solutions based on artefacts akin to their expertise and understanding of ideal solution

EzWeb/FAST: Reporting on a Successful Mashup-based Solution for Developing and Deploying Composite Applications in the Upcoming Web of Services

Service oriented architectures (SOAs) based on Web Services have attracted a great interest and IT investments during the last years, principally in the context of business-to-business integration within corporate intranets. However, they are nowadays evolving to break through enterprise boundaries, in a revolutionary attempt to make the approach pervasive, leading to what we call a user-centric SOA, i.e. a SOA conceived as a Web of Services made up of compositional resources that empowers end-users to ubiquitously exploit these resources by collaboratively remixing them. In this paper we explore the architectural basis, technologies, frameworks and tools considered necessary to face this novel vision of SOA. We also present the rationale behind EzWeb/FAST: an undergoing EU funded project whose first outcomes could serve as a preliminary proof of concep

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Facilitating Creativity in Collaborative Work with Computational Intelligence Software

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectives-contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015

Conceptual development of custom, domain-specific mashup platforms

Despite the common claim by mashup platforms that they enable end-users to develop their own software, in practice end-users still don't develop their own mashups, as the highly technical or inexistent user bases of today's mashup platforms testify. The key shortcoming of current platforms is their general-purpose nature, that privileges expressive power over intuitiveness. In our prior work, we have demonstrated that a domainspecific mashup approach, which privileges intuitiveness over expressive power, has much more potential to enable end-user development (EUD). The problem is that developing mashup platforms - domain-specific or not - is complex and time consuming. In addition, domain-specific mashup platforms by their very nature target only a small user basis, that is, the experts of the target domain, which makes their development not sustainable if it is not adequately supported and automated. With this article, we aim to make the development of custom, domain-specific mashup platforms costeffective. We describe a mashup tool development kit (MDK) that is able to automatically generate a mashup platform (comprising custom mashup and component description languages and design-time and runtime environments) from a conceptual design and to provision it as a service. We equip the kit with a dedicated development methodology and demonstrate the applicability and viability of the approach with the help of two case studies. © 2014 ACM

Supporting self-regulated learning

Self-regulated learning (SRL) competences are crucial for lifelong learning. Their cultivation requires the right balance between freedom and guidance during the learning processes. Current learning systems and approaches, such as personal learning environments, give overwhelming freedom, but also let weak learners alone. Other systems, such as learning management systems or adaptive systems, tend to institutionalise learners too much, which does not support the development of SRL competences. This chapter presents possibilities and approaches to support SRL by the use of technology. After discussing the theoretical background of SRL and related technologies, a formal framework is presented that describes the SRL process, related competences, and guidelines. Furthermore, a variety of methods is presented, how learners can be supported to learn in a self-regulated way

Software expert discovery via knowledge domain embeddings in a collaborative network

© 2018 Elsevier B.V. Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence

Mining and quality assessment of mashup model patterns with the crowd: A feasibility study

Pattern mining, that is, the automated discovery of patterns from data, is a mathematically complex and computationally demanding problem that is generally not manageable by humans. In this article, we focus on small datasets and study whether it is possible to mine patterns with the help of the crowd by means of a set of controlled experiments on a common crowdsourcing platform. We specifically concentrate on mining model patterns from a dataset of real mashup models taken from Yahoo! Pipes and cover the entire pattern mining process, including pattern identification and quality assessment. The results of our experiments show that a sensible design of crowdsourcing tasks indeed may enable the crowd to identify patterns from small datasets (40 models). The results, however, also show that the design of tasks for the assessment of the quality of patterns to decide which patterns to retain for further processing and use is much harder (our experiments fail to elicit assessments from the crowd that are similar to those by an expert). The problem is relevant in general to model-driven development (e.g., UML, business processes, scientific workflows), in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices, organizational conventions, or technical choices, that modelers can benefit from when designing their own models