4 research outputs found

    A centralised cloud services repository (CCSR) framework for optimal cloud service advertisement discovery from heterogenous web portals

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    © 2013 IEEE. A cloud service marketplace is the first point for a consumer to discovery, select and possible composition of different services. Although there are some private cloud service marketplaces, such as Microsoft Azure, that allow consumers to search service advertainment belonging to a given vendor. However, due to an increase in the number of cloud service advertisement, a consumer needs to find related services across the worldwide web (WWW). A consumer mostly uses a search engine such as Google, Bing, for the service advertisement discovery. However, these search engines are insufficient in retrieving related cloud services advertainments on time. There is a need for a framework that effectively and efficiently discovery of the related service advertisement for ordinary users. This paper addresses the issue by proposing a user-friendly harvester and a centralised cloud service repository framework. The proposed Centralised Cloud Service Repository (CCSR) framework has two modules - Harvesting as-a-Service (HaaS) and the service repository module. The HaaS module allows users to extract real-time data from the web and make it available to different file format without the need to write any code. The service repository module provides a centralised cloud service repository that enables a consumer for efficient and effective cloud service discovery. We validate and demonstrate the suitability of our framework by comparing its efficiency and feasibility with three widely used open-source harvesters. From the evaluative result, we observe that when we harvest a large number of services advertisements, the HaaS is more efficient compared with the traditional harvesting tools. Our cloud services advertisements dataset is publicly available for future research at: http://cloudmarketregistry.com/cloud-market-registry/home.html

    Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

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    There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce—onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological–organisation–environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption

    A Graph-Based Web Services Discovery Framework for IoT EcoSystem

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    Nowadays, the Internet of Things (IoT) represents an important topic and research domain with multiple objectives. However, most IoTs communicate poorly across the multitude of network interfaces. It should be preferably used a single universal application layer protocol for the devices and services interconnection, regardless of how they are physically connected. The IoT paradigm boosts the device connectivity and the user accessibility benefits of services introduced within the network of connected objects associated with a context-awareness. Within this frame of reference, Web service is the appropriate technological approach to exhibit a set of related IoT functionalities loosely coupled with other services discovered or composed through the Web. In this work, we consider the heterogeneity of connecting technologies for IoT and the applications and devices integration in a single interoperable framework as a research objective. With this in mind, we introduce a five layers multigraph model for Web Services discovery and recommendation, and we address Web services-based applications for IoT data integration. The launched service discovery process permits the interaction between the user/application and the IoT environment. In this context, the choice of suitable services represents a challenge that covers the functionality and the required quality to combine composite services, namely mashups for IoT data management and interconnection. For proof of concept, we test a RESTful Web Services framework as an experimental platform to animate a graph-based approach for dynamic IoT services discovery. We develop a recommender system that performs graph analytics to produce a set of services according to the user's request. The quality of the recommendation process is evaluated by analyzing the correlation of user satisfaction

    A Centralised Cloud Services Repository (CCSR) Framework for Optimal Cloud Service Advertisement Discovery From Heterogenous Web Portals

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