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Parallel and Distributed Intelligent Systems

并行分布式智能系统

基本信息

批准号：
89-2012
负责人：
Bhavsar, Virendrakumar
金额：
$ 1.02万
依托单位：
University of New Brunswick
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2015
资助国家：
加拿大
起止时间：
2015-01-01 至 2016-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568093
关键词：
Parallel Distributed Intelligent Systems

项目摘要

PARALLEL AND DISTRIBUTED INTELLIGENT SYSTEMS The long-term objective of our work has been to develop efficient models, algorithms and implementations of parallel and distributed intelligent systems. Our goal has been not only to develop and experiment with new algorithms, but to develop operational software systems that can be employed in the real world as well. Whenever opportunities arise, the outcomes of our research will be commercialized and integrated into products. We propose to generalize the concept of the weighted tree similarity proposed earlier by us, to the weighted graph similarity and exploit it to carry out semantic matching, matchmaking and semantic search. Graph algorithms are more complex than tree algorithms. Therefore, in order to handle large data sets, we will develop parallel and distributed algorithms suitable for multi-core processors, clusters, grids and clouds to drastically reduce computing time. Our proposed work, which generalizes our previous work to provide more precise matching, has even greater potential for real world applications. The applications in e-Business would result in much more precise matching than simple keyword matches used in Kijiji and similar systems. In e-Health domain, the proposed approach promises better social networking among patients and more precise results from querying patient record histories. Finally, the proposed approach will give highly relevant results to user queries compared to simple keyword-based searches used in search engines like Google.

并行与并行智能系统我们工作的长期目标是开发有效的模型，算法和并行和分布式智能系统的实现。我们的目标不仅是开发和试验新的算法，而且是开发可用于真实的世界的操作软件系统。只要有机会，我们的研究成果将被商业化并整合到产品中。我们建议推广的概念，加权树相似度提出我们之前，加权图相似度，并利用它进行语义匹配，匹配和语义搜索。图算法比树算法更复杂。因此，为了处理大型数据集，我们将开发适用于多核处理器、集群、网格和云的并行和分布式算法，以大幅减少计算时间。我们提出的工作，概括了我们以前的工作，以提供更精确的匹配，有更大的潜力为真实的世界的应用。电子商务中的应用程序将导致比Kijiji和类似系统中使用的简单关键字匹配更精确的匹配。在电子健康领域，所提出的方法承诺更好的社交网络之间的病人和更精确的结果，从查询病历的历史。最后，与Google等搜索引擎中使用的基于简单关键字的搜索相比，所提出的方法将为用户查询提供高度相关的结果。