A cloud-based platform to develop context-aware mobile applications by domain experts

Lamsfus, Carlos; Alzua-Sorzabal, Aurkene

doi:10.1016/j.csi.2015.08.009

Cited by 14 publications

(19 citation statements)

References 24 publications

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“…Another related approach for EUD from desktop environments is presented in [31], where the authors present a cloud-based development platform of context-aware mobile services to be consumed as native applications. The platform is accessible through a web-based application, where the producer can associate a set of context values (specific locations, areas, times, dates, etc.)…”

Section: Related Workmentioning

confidence: 99%

An End User Development Approach for Mobile Web Augmentation

Bosetti

Firmenich

Gordillo

et al. 2017

Mobile Information Systems

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The trend towards mobile devices usage has put more than ever the Web as a ubiquitous platform where users perform all kind of tasks. In some cases, users access the Web with "native" mobile applications developed for well-known sites, such as LinkedIn, Facebook, Twitter, etc. These native applications might offer further (e.g. location-based) functionalities to their users in comparison with their corresponding Web sites, because they were developed with mobile features in mind. However, most Web applications have not this native mobile counterpart and users access them using browsers in the mobile device. Users might eventually want to add mobile features on these Web sites even though those features were not supported originally. In this paper we present a novel approach to allow end users to augment their preferred Web sites with mobile features. This end-user approach is supported by a framework for mobile Web augmentation that we describe in the paper. We also present a set of supporting tools and a validation experiment with end users.

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Section: Related Workmentioning

confidence: 99%

An End User Development Approach for Mobile Web Augmentation

Bosetti

Firmenich

Gordillo

et al. 2017

Mobile Information Systems

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“…In fact, the selected works focus on the recommendation (or selection) of services from different cloud platforms in MCC that are affected by user preferences [104], the current context of mobile devices [105][106][107], and social networks [7,108,109]. That means that there are multiple sources of information that are used to understand the real users' needs, and at the same time, they make the recommendation effectiveness relative, since the nature of these data is unstable and relative.…”

Section: Cloud Service Recommendationmentioning

confidence: 99%

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Bangui

Bühnová

et al. 2017

JSAN

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Abstract:Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers' computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process.

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“…Nevertheless, the high-level context usually could not be directly obtained from these sensors/devices. There is still a major gap that separates the data provided by sensing devices and the high-level contextual interpretation required for m-learning (Martin, Lamsfus, & Alzua-Sorzabal, 2016). To recognize and generate high-level contextual information, statistical, syntactic, and description-based approaches are proposed respectively (Ryoo & Aggarwal, 2009).…”

Section: Introductionmentioning

confidence: 99%

A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning

Pan

Ling

Zhu

et al. 2017

EURASIA J MATH SCI T

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In m-learning environments, context-awareness is for wide use where learners' situations are varied, dynamic and unpredictable. We are facing the challenge of requirements of both generality and depth in generating and processing high-level context. In this paper, we present a social approach which exploits social dynamics and social computing for generating high-level context. It is a novel and generic paradigm where the crowds of learners in m-learning environments directly engage in creating contents about high-level context and interactions by social tagging, and these contents and interactions are further explored to discover more implicit and complex high-level contextual information. We present the concept model, the context representation, the context matrix, and the context retrieval method. We evaluate our approach by a social simulation based experiment. The experimental results demonstrate that the context retrieval performance is improved in both the accuracy and the diversity, and validate that the proposed social approach is effective for generating high-level context.

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A cloud-based platform to develop context-aware mobile applications by domain experts

Cited by 14 publications

References 24 publications

An End User Development Approach for Mobile Web Augmentation

An End User Development Approach for Mobile Web Augmentation

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning

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