Quality-aware mashup composition: issues, techniques and tools

Web mashups are a new generation of applications based on the composition of ready-to-use, heterogeneous components. In different contexts, ranging from the consumer Web to Enterprise systems, the potential of this new technology is to make users evolve from passive receivers of applications to actors actively involved in the creation of their artifacts, thus accommodating the inherent variability of the users’ needs. Current advances in mashup technologies are good candidates to satisfy this requirement. However, some issues are still largely unexplored. In particular, quality issues specific for this class of applications, and the way they can guide the users in the identification of adequate components and composition patterns, are neglected. This paper discusses quality dimensions that can capture the intrinsic quality of mashup components, as well as the components’ capacity to maximize the quality and the userperceived value of the overall composition. It also proposes an assisted composition process in which quality becomes the driver for recommending to the users how to complete mashups, based on the integration of quality assessment and recommendation techniques within a tool for mashup development

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

Serving deep learning models in a serverless platform

Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs

An Effective End-User Development Approach Through Domain-Specific Mashups for Research Impact Evaluation

Over the last decade, there has been growing interest in the assessment of the performance of researchers, research groups, universities and even countries. The assessment of productivity is an instrument to select and promote personnel, assign research grants and measure the results of research projects. One particular assessment approach is bibliometrics i.e., the quantitative analysis of scientific publications through citation and content analysis. However, there is little consensus today on how research evaluation should be performed, and it is commonly acknowledged that the quantitative metrics available today are largely unsatisfactory. A number of different scientific data sources available on the Web (e.g., DBLP, Google Scholar) that are used for such analysis purposes. Taking data from these diverse sources, performing the analysis and visualizing results in different ways is not a trivial and straight forward task. Moreover, people involved in such evaluation processes are not always IT experts and hence not capable to crawl data sources, merge them and compute the needed evaluation procedures. The recent emergence of mashup tools has refueled research on end-user development, i.e., on enabling end-users without programming skills to produce their own applications. We believe that the heart of the problem is that it is impractical to design tools that are generic enough to cover a wide range of application domains, powerful enough to enable the specification of non-trivial logic, and simple enough to be actually accessible to non-programmers. This thesis presents a novel approach for an effective end-user development, specifically for non-programmers. That is, we introduce a domain-specific approach to mashups that "speaks the language of users"., i.e., that is aware of the terminology, concepts, rules, and conventions (the domain) the user is comfortable with.Comment: This PhD dissertation consists of 206 page

Towards a Cyber-Physical Manufacturing Cloud through Operable Digital Twins and Virtual Production Lines

In last decade, the paradigm of Cyber-Physical Systems (CPS) has integrated industrial manufacturing systems with Cloud Computing technologies for Cloud Manufacturing. Up to 2015, there were many CPS-based manufacturing systems that collected real-time machining data to perform remote monitoring, prognostics and health management, and predictive maintenance. However, these CPS-integrated and network ready machines were not directly connected to the elements of Cloud Manufacturing and required human-in-the-loop. Addressing this gap, we introduced a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) that bridges a gap between physical machines and virtual space in 2017. CPMC virtualizes machine tools in cloud through web services for direct monitoring and operations through Internet. Fundamentally, CPMC differs with contemporary modern manufacturing paradigms. For instance, CPMC virtualizes machining tools in cloud using remote services and establish direct Internet-based communication, which is overlooked in existing Cloud Manufacturing systems. Another contemporary, namely cyber-physical production systems enable networked access to machining tools. Nevertheless, CPMC virtualizes manufacturing resources in cloud and monitor and operate them over the Internet. This dissertation defines the fundamental concepts of CPMC and expands its horizon in different aspects of cloud-based virtual manufacturing such as Digital Twins and Virtual Production Lines. Digital Twin (DT) is another evolving concept since 2002 that creates as-is replicas of machining tools in cyber space. Up to 2018, many researchers proposed state-of-the-art DTs, which only focused on monitoring production lifecycle management through simulations and data driven analytics. But they overlooked executing manufacturing processes through DTs from virtual space. This dissertation identifies that DTs can be made more productive if they engage directly in direct execution of manufacturing operations besides monitoring. Towards this novel approach, this dissertation proposes a new operable DT model of CPMC that inherits the features of direct monitoring and operations from cloud. This research envisages and opens the door for future manufacturing systems where resources are developed as cloud-based DTs for remote and distributed manufacturing. Proposed concepts and visions of DTs have spawned the following fundamental researches. This dissertation proposes a novel concept of DT based Virtual Production Lines (VPL) in CPMC in 2019. It presents a design of a service-oriented architecture of DTs that virtualizes physical manufacturing resources in CPMC. Proposed DT architecture offers a more compact and integral service-oriented virtual representations of manufacturing resources. To re-configure a VPL, one requirement is to establish DT-to-DT collaborations in manufacturing clouds, which replicates to concurrent resource-to-resource collaborations in shop floors. Satisfying the above requirements, this research designs a novel framework to easily re-configure, monitor and operate VPLs using DTs of CPMC. CPMC publishes individual web services for machining tools, which is a traditional approach in the domain of service computing. But this approach overcrowds service registry databases. This dissertation introduces a novel fundamental service publication and discovery approach in 2020, OpenDT, which publishes DTs with collections of services. Experimental results show easier discovery and remote access of DTs while re-configuring VPLs. Proposed researches in this dissertation have received numerous citations both from industry and academia, clearly proving impacts of research contributions

Implementation of end-user development success factors in mashup development environments

[EN] The Future Internet is expected to be composed of a mesh of interoperable web services accessed from all over the Web. This approach has been supported by many software providers who have provided a wide range of mash up tools for creating composite applications based on components prepared by the respective provider. These tools aim to achieve the end-user development (EUD) of rich internet applications (RIA); however, most, having failed to meet the needs of end users without programming knowledge, have been unsuccessful. Thus, many studies have investigated success factors in order to propose scales of success factor objectives and assess the adequacy of mashup tools for their purpose. After reviewing much of the available literature, this paper proposes a new success factor scale based on human factors, human-computer interaction (HCI) factors and the specialization-functionality relationship. It brings together all these factors, offering a general conception of EUD success factors. The proposed scale was applied in an empirical study on current EUD tools, which found that today's EUD tools have many shortcomings. In order to achieve an acceptable success rate among end users, we then designed a mashup tool architecture, called FAST-Wirecloud, which was built taking into account the proposed EUD success factor scale. The results of a new empirical study carried out using this tool have demonstrated that users are better able to successfully develop their composite applications and that FAST-Wirecloud has scored higher than all the other tools under study on all scales of measurement, and particularly on the scale proposed in this paper. (C) 2016 Elsevier B.V. All rights reserved.This research was partially supported by the European Union co-funded IST projects FAST: Fast and Advanced Storyboard Tools (GA 216048), FI-WARE: Future Internet Core Platform (GA 285248) and FI-CORE: Future Internet - Core (GA 632893). The FI-WARE and FI-CORE projects are part of the European Commission's Futuree Internet Public-Private Partnership (FI-PPP) initiative.Lizcano, D.; López, G.; Soriano, J.; Lloret, J. (2016). Implementation of end-user development success factors in mashup development environments. Computer Standards & Interfaces. 47:1-18. https://doi.org/10.1016/j.csi.2016.02.006S1184