1,426 research outputs found

    Towards a Visualization Framework for Service Selection in Cloud e-Marketplaces

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    In spite of the success of many commercial cloud service e-marketplaces the search results from these platforms are usually presented as an unordered list of icons representing the services that best fit users’ keyword-based queries. The drawback of such presentation mechanisms is that users are not able to immediately discriminate among the cloud services for easy decision making. A number of cloud service selection frameworks have been proposed; however, some of these frameworks do not enable users make comparisons among services. In this paper, we introduce a visualization framework for cloud service selection. Our framework takes into cognizance the set of cloud services that matches a user’s request and based on QoS attributes, users can interact with the results via bubble graph visualization to compare and contrast the search results to ascertain the best alternative. The bubble graph enables the exploration of services in a unified view of the QoS space, exhibiting both high object coherence and correlation. Result from our experiments shows that our framework simplifies decision making as users can identify services that best fit their requirements quicker and easier compared to tabular format

    Integrating fuzzy theory and visualization for QoS-aware selection of SaaS in cloud e-Marketplaces

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    Most cloud service e-marketplaces incorporate basic features like search and billing but lack more sophisticated elements that optimise users’ experience. The cognitive demands of searching for and evaluating multiple cloud SaaS along multiple QoS criteria can be overwhelming, giving rise to what Alvin Toffler called choice overload. There is a need to integrate mechanisms that handles the vagueness that characterises the human decision-making process when finding suitable services. The objective of this paper is to reduce cognitive overload during cloud service selection in e-marketplaces by employing low cognitive demanding tools that leverage the dynamics of human expressions. We proposed a QoS-aware SaaS ranking and selection framework that integrates fuzzy theory and information visualisation for optimal decision-making in cloud e-marketplaces. An illustrative case study of Customer-Relationship-Management-as-a-Service e-marketplace demonstrated the framework’s plausibility. The demonstration shows that our framework is a viable approach to rank and select SaaS in cloud e-marketplaces ina way that satisfactorily serves both the users of the platform and can potentially drive the business objectives of the e-marketplace

    Towards a Fuzzy-oriented Framework for Service Selection in Cloud e-Marketplaces

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    The growing popularity of cloud services requires service selection platforms that offer enhanced user experience in terms of handling complex user requirements, elicitation of quality of service (QoS) requirements, and presentation of search results to aid decision making. So far, none of the existing cloud service selection approaches has provided a framework that wholly possesses these attributes. In this paper, we proposed a fuzzy-oriented framework that could facilitate enhanced user experience in cloud emarketplaces through formal composition of atomic services to satisfy complex user requirements, elicitation and processing of subjective user QoS requirements, and presentation of search results in a visually intuitive way that aids users’ decision making. To do this, an integration of key concepts such as constrained-based reasoning on feature models, fuzzy pairwise comparison of QoS attributes, fuzzy decision making, and information visualization have been used. The applicability of the framework is illustrated with an example of Customer Relationship Management as a Service

    Design of a QoS-based Framework for Service Ranking and Selection in Cloud E-marketplaces

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    In most existing commercial cloud e-marketplaces, finding a suitable cloud service to perform user's objectives can be cognitively demanding and potentially affects the user satisfaction of both the process and outcome of decision making. Most existing cloud selection techniques have not sufficiently addressed the problem of service choice overload in a manner, that provides means that elicits subjective user preferences. Besides, only a few of these techniques suffice in situations where there are a large number of services to be evaluated and the results are presented in textual formats, either in a list or tables, which does not provide any means of comparison of results returned. Based on a comparative review of existing service selection techniques, a set of requirements was identified to guide the design of cloud service selection framework that would suffice in a cloud e-marketplace context. A cloud service selection framework was formulated that encapsulates the set of requirements. The increase in the number of available services on the e-marketplace leaves the users in the dilemma of which service to select, particularly when the services perform equivalent functionalities and may only differ with respect to their quality of service (QoS) attributes. The proposed framework is a viable proposition for the reduction service choice overload in cloud service e-marketplaces

    Using Constraint Reasoning on Feature Models to Populate Ecosystem-driven Cloud Services e- Marketplace

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    Service providers leverage cloud ecosystems and cloud e-marketplaces to increase the business value of their services and reach a wider range of service users. A cloud ecosystem enable participating services to combine with other services, along their QoS properties; while the e-marketplace provides an environment where atomic services interconnect in unprecedented ways to be traded on the marketplace platform. Noting the unprofitability, impracticality and error-prone nature of performing ad hoc service combination of atomic services, the concern addressed in this technical report is how to guide the combination of atomic services participating in an ecosystem in a seamless manner. In this technical report, we proposed the use of feature models to model the inter-relationships and constraints among the atomic services, which is transformed into a constraint satisfaction problem and off-the-shelve constraint solvers are used to determining valid combinations. The collection of valid combinations become the blueprint that guides service composition and populates the e-marketplace service directory; users can then make service selection decisions based on the list. The applicability of the approach proposed in this report is demonstrated via an example of Customer relationship management as a service ecosystem

    Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

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    Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares NaĂŻve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset

    Business Intelligence Tools: overview and comparison study of analytic solutions

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBusiness intelligence (BI) proves to bring the companies competitive advantages, providing for an easier and clear decision-making process through the analysis of data. The growth in the BI tools market is clear and can be verified by the increase in tools being offered and the number of users looking for the right system to apply in their companies. However, it can be overwhelming to choose one tool considering the ocean of BI tools available in the market. It is key to understand which tool applies the best to certain business specifications before committing to a sometimes rather considerable investment. This work aims to give business intelligence users proximity to some business specifications and the tools that most apply to them. This is possible through the creation of use cases that exemplify business needs of fictitious companies and by using six BI tools to then test how the tools answer to each business needs. The work carried out holds out a capability matrix as output that compares how each tool works when facing a specific topic, to understand at the end which is the best tool for each use case

    Data Spaces

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    This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical
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