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Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering...
The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed...
Mobile cloud-based learning is a novel trend that brings many advantages to distributed learners to achieve collaborative learning, but it still lacks of mechanisms to enhance their teamwork performances. To make up that shortcoming, combining the features of cloud environment, we have identified a learning flow, a specification of workflow, based on Kolb team learning experience. This novel learning...
The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of the decade. Providing efficient mechanisms for optimized data-intensive services will become critical to meet the expected growing demand. In order to create a cost minimizing data-intensive service composition solution, we design two steps and two negotiation...
One of the challenges for the P2P-based service composition process is how to effectively discover and select the most appropriate peers to execute the service applications when considering multiple properties of the requested services. Different ontology-based e-service profiles have been proposed to facilitate handling multiple properties and to enhance the service oriented process in order to achieve...
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