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Modern development of computing systems caters the collaboration of human-based resources together with machine-based resources as active compute units. Those units can be dynamically provisioned on-demand for solving complex tasks, such as observed in collaborative applications, crowd sourced applications, and human task workflows. Such collaborations involve very diverse compute units, which have...
In this paper, we explore the benefits of automatically determining the degree of parallelism used to perform genetic mutation calling in a hybrid cloud environment. We propose algorithms to automatically control both the hiring of hybrid cloud resources and the selection of the degree of parallelism employed in analysis tasks executed against that cloud. Using the Broad Institute's Genome Analysis...
Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics...
Smart cities encompass services in diverse business and technological domains. Presently, most of these services are delivered through domain-specific, tightly coupled systems, which entail limited scalability and extensibility. The authors propose Web-scale service delivery that addresses these limitations and encourage the creation of novel services based on a domain-independent, cloud-based service-delivery...
In service-oriented enterprise architecture, provisioning business services is made on top of IT processes, which should be elastic amid the availability of computing resources and the variation of user demand. In addition, the provisioning depends on human resources utilized and is constrained by the business objectives (e.g. a goal) plus coarse-grained constraints (e.g. an order in which business...
Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled...
With diverse and rich offerings from cloud computing providers in the open cloud market, scientists have great opportunities to design and conduct complex applications by utilizing and combining computational resources, software components and data sources in elastic manners. While existing techniques focus mainly on resource elasticity in single cloud infrastructure, scientists expect to design their...
Traditional data quality engineering techniques, often used and deployed within a single enterprise environment, are inadequate to cope with the rapid change of data, with a multitude of quality degrees, to be used in contemporary business models. The emerging cloud computing paradigm could potentially offer high-quality, composable data and techniques, under the Software-as-a-Service (SaaS), Data-asa-Service...
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