Titlebook: Web Information Systems Engineering - WISE 2010; 11th International C Lei Chen,Peter Triantafillou,Torsten Suel Conference proceedings 2010

Morose

Building Web Services Middleware with Predictable Service Executiong web service middleware execute requests in a . manner. While this allows them to achieve a higher throughput, it results in highly unpredictable execution times, rendering them unsuitable for applications that require predictability in execution. The guidelines, algorithms and techniques presented

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入伍儀式

微生物

On Identifying and Reducing Irrelevant Information in Service Composition and Execution goals. In the last years both industrial and academic researchers have investigated the way in which quality of services can be described, matched, composed and monitored for service selection and composition. However, very few of them have considered the problem of evaluating and certifying the qu

Ingenuity

確定方向

Propagation of Data Protection Requirements in Multi-stakeholder Web Services Systemser-protected or blindly over-protected when the receivers of the data are not known directly to the sender. This paper presents an approach to aiding collaborative partner services to properly protect each other’s data. Each partner derives an adequate protection mechanism for each message it sends

Arthritis

enflame

Refining Graph Partitioning for Social Network Clusteringocial network analysis arouses the new research interest on graph clustering. Social networks differ from conventional graphs in that they exhibit some key properties which are largely neglected in popular partitioning algorithms. In this paper, we propose a novel framework for finding clusters in r

POINT

Refining Graph Partitioning for Social Network Clusteringocial network analysis arouses the new research interest on graph clustering. Social networks differ from conventional graphs in that they exhibit some key properties which are largely neglected in popular partitioning algorithms. In this paper, we propose a novel framework for finding clusters in r

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