Incorporating Business Intelligence in Cloud Marketplaces

Gatzioura, Anna; Menychtas, Andreas; Moulos, Vrettos; Varvarigou, Theodora

doi:10.1109/ispa.2012.68

Cited by 3 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This collaboration means that IT services can be aggregated in different ways to deliver more value-adding functionalities (Baek et al, 2014). The popularity of cloud computing services will culminate in the rise of e-marketplaces for buying and selling cloud services, enabling the search, discovery, selection of, and payment for cloud services under one e-market infrastructure (Akolkar et al, 2012;Blasco et al, 2020;Gatzioura et al, 2012).…”

Section: Cloud Eco-systems and E-marketplacesmentioning

confidence: 99%

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

Ezenwoke

Adigun

2021

Cogent Engineering

View full text Add to dashboard Cite

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 in ABOUT THE AUTHOR

show abstract

Section: Cloud Eco-systems and E-marketplacesmentioning

confidence: 99%

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

Ezenwoke

Adigun

2021

Cogent Engineering

View full text Add to dashboard Cite

show abstract

“…The growing popularity of cloud services requires service selection platforms that enable the composition of atomic services to satisfy complex user requirements, and highlight the quality of services (QoS) attributes of these value-added services under one e-marketplace structure (Akolkar et al, 2012;Gatzioura et al, 2012). Despite their successes, commercial cloud e-marketplaces (e.g.…”

Section: Towards a Fuzzy-oriented Framework For Service Selection In mentioning

confidence: 99%

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

Ezenwoke

Daramola

Adigun

2017

Proceedings of the 7th International Conference on Cloud Computing and Services Science

View full text Add to dashboard Cite

Abstract: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.

show abstract

“…In this section, we describe the findings from our SLR. System design  Data structure in the cloud [49]  Business process for incorporating BI in cloud market [78]  Business process for a generic value network [77] Taxonomy Table 1: Synthesis of the literature review…”

Section: Mapping Current Research On Bi In the Cloudmentioning

confidence: 99%

“…Gatzioura et al focuses on implementation of a business model process (system design) and a tool (implemented system) for incorporating business intelligence in cloud marketplaces [78]. Based on the individual requirements and using profile of each particular cloud customer, this system allows answering the question: "Which offering should I buy"?…”

Section: Analyticsmentioning

confidence: 99%

“… Tool to accomplish the BI tasks in the cloud [60] [78] Example  Example of BI migration Regarding BI in the cloud in general, the literature proposed implementation of tools that can enable users to accomplish the BI tasks in the cloud [60] [78]. We propose to define a taxonomy of BI migration, including guidelines to properly configure cloud resources, and examples of BI migration.…”

Section: Implemented Systemmentioning

confidence: 99%

See 1 more Smart Citation

Design science research contribution to business intelligence in the cloud — A systematic literature review

Mwilu

Comyn-Wattiau

Prat

2016

Future Generation Computer Systems

View full text Add to dashboard Cite

Business intelligence (BI) helps managers make informed decisions. In the age of big data, BI technology provides essential support for decision making. Cloud computing also attracts many organizations because of its potential: ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services). This paper focuses on the deployment of BI in the cloud, from the vantage point of design science research (DSR). We produce a state of the art of research pertaining to BI in the cloud, following the methodology of systematic literature review. This literature review especially exhibits the different artifacts proposed by design science researchers regarding BI in the cloud. To structure the literature review, we propose a framework composed of two dimensions: artifact type and BI step. In particular, we propose a typology of artifact types, refining the coarse-grained typology commonly used in DSR. We use the two-dimensional framework both to map the current state of DSR regarding BI in the cloud, and to elicit future research avenues in terms of design science artifacts for BI in the cloud. The contribution is threefold: the literature review may help DSR researchers get an overview of this active research domain; the two-dimensional framework facilitates the understanding of different research streams; finally, the proposed future topics may guide researchers in identifying promising research avenues.

show abstract

Incorporating Business Intelligence in Cloud Marketplaces

Cited by 3 publications

References 15 publications

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

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

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

Design science research contribution to business intelligence in the cloud — A systematic literature review

Contact Info

Product

Resources

About