物联网语义中间件:实现设备互操作性

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"Semantic Middleware for the Internet of Things - 美国富士通实验室关于物联网中间件的综述性论文" 本文由Zhexuan Song、Alvaro A. Cárdenas和Ryusuke Masuoka共同撰写,发表于富士通美洲实验室,主要探讨了物联网(IoT)环境中的语义中间件解决方案。物联网是将家用电器、消费电子产品和传感器网络等设备接入互联网的体系,但设备间的异构性和多样性导致了互操作性和可组合性的重大挑战。传统的解决互操作性的方式是制定标准,然而,现有的多种标准之间往往存在不兼容问题。 针对这一问题,作者提出了一种应用层的解决方案,即利用语义中间件来提升互操作性。这个创新点在于,他们建议利用现有的设备规格来提供语义,并将这些语义动态地封装到他们的中间件中,形成语义服务。通过这种方式,设备的语义可以被理解并用于不同设备之间的通信。 借助于语义网技术(如OWL、RDF等),用户能够创建并执行涉及多个异构设备的复杂任务。这一框架的关键优势在于,它能自动实现设备间的互操作性,而无需对原始设备进行任何修改,从而降低了集成的复杂性和成本。 此外,论文还可能深入讨论了如何利用本体(Ontology)来描述和连接不同设备的服务,以及如何通过推理机制来解决设备间的语义差异。本体在物联网中起到了关键作用,它提供了一种结构化的语言,使得机器能够理解和处理设备的抽象概念,进而实现智能决策和自动化操作。 论文的贡献可能还包括实际案例分析或实验结果,以证明该语义中间件框架的有效性和实用性。通过这种方式,作者为物联网的互操作性提供了一个新的视角,不仅解决了标准不兼容的问题,还增强了系统的灵活性和可扩展性。 这篇论文对于理解物联网环境中的语义中间件及其在提升设备互操作性方面的作用具有重要意义,对于物联网领域的研究者和开发者来说,是一个宝贵的参考资料。

精简下面表达:Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

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