As a widely used fault tolerance technique, checkpointing has evolved into several schemes: independent, coordinated, and communication-induced (CIC). Independent and coordinated checkpointing have been adopted in many works on fault tolerant mobile agent (MA) systems. However, CIC, a flexible, efficient, and scalable checkpointing scheme, has not been applied to MA systems. Based on the analysis of the behavior of mobile agent, we argue that CIC is a well suited checkpointing scheme for MA systems. CIC not only establishes the consistent recovery lines efficiently but also integrates well with the independent checkpointing for reliable MA migration. Here, we propose an important improvement to CIC, referred to as the deferred message processing based CIC algorithm (DM-CIC), which achieves higher efficiency by exempting the CIC algorithm from making the forced checkpoints in MA systems. Through simulation, we find out that DM-CIC is stable and better suited to large scale MA systems.
{"title":"CIC: an integrated approach to checkpointing in mobile agent systems","authors":"Jin Yang, Jiannong Cao, Weigang Wu","doi":"10.1109/SKG.2006.33","DOIUrl":"https://doi.org/10.1109/SKG.2006.33","url":null,"abstract":"As a widely used fault tolerance technique, checkpointing has evolved into several schemes: independent, coordinated, and communication-induced (CIC). Independent and coordinated checkpointing have been adopted in many works on fault tolerant mobile agent (MA) systems. However, CIC, a flexible, efficient, and scalable checkpointing scheme, has not been applied to MA systems. Based on the analysis of the behavior of mobile agent, we argue that CIC is a well suited checkpointing scheme for MA systems. CIC not only establishes the consistent recovery lines efficiently but also integrates well with the independent checkpointing for reliable MA migration. Here, we propose an important improvement to CIC, referred to as the deferred message processing based CIC algorithm (DM-CIC), which achieves higher efficiency by exempting the CIC algorithm from making the forced checkpoints in MA systems. Through simulation, we find out that DM-CIC is stable and better suited to large scale MA systems.","PeriodicalId":210294,"journal":{"name":"2006 Semantics, Knowledge and Grid, Second International Conference on","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122997336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although English has become the standard language in various areas, most people do not use it in local activities. To increase the mutual understanding of different cultures with different languages, it is essential to build a language infrastructure on top of the Internet that improves the accessibility and usability of existing online language services so that users can create new cross-language services for their communities. To realize this infrastructure, this paper proposes the language grid. The language grid consists of the ”horizontal language grid,” which connects the standard languages of nations, and ”vertical language grid,” which combines the language services generated by communities. This approach can facilitate intercultural collaboration through the Internet, such as international online collaborative learning.
{"title":"Infrastructure for language service composition","authors":"Yohei Murakami, T. Ishida, Takao Nakaguchi","doi":"10.1109/SKG.2006.58","DOIUrl":"https://doi.org/10.1109/SKG.2006.58","url":null,"abstract":"Although English has become the standard language in various areas, most people do not use it in local activities. To increase the mutual understanding of different cultures with different languages, it is essential to build a language infrastructure on top of the Internet that improves the accessibility and usability of existing online language services so that users can create new cross-language services for their communities. To realize this infrastructure, this paper proposes the language grid. The language grid consists of the ”horizontal language grid,” which connects the standard languages of nations, and ”vertical language grid,” which combines the language services generated by communities. This approach can facilitate intercultural collaboration through the Internet, such as international online collaborative learning.","PeriodicalId":210294,"journal":{"name":"2006 Semantics, Knowledge and Grid, Second International Conference on","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123042446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a prediction approach that combines grey prediction model with three-layer computing architecture in Web environment. It presents a refined prediction formula to enhance the scalability and usage scope of the Web prediction systems. It layouts a three layer architecture for the Web prediction system to reduce the network traffic and maintain load balance. A traffic decision support system is presented to illustrate the proposed approach.
{"title":"A data mining approach based on Grey prediction model in web environment","authors":"Hui Peng","doi":"10.1109/SKG.2006.2","DOIUrl":"https://doi.org/10.1109/SKG.2006.2","url":null,"abstract":"This paper proposes a prediction approach that combines grey prediction model with three-layer computing architecture in Web environment. It presents a refined prediction formula to enhance the scalability and usage scope of the Web prediction systems. It layouts a three layer architecture for the Web prediction system to reduce the network traffic and maintain load balance. A traffic decision support system is presented to illustrate the proposed approach.","PeriodicalId":210294,"journal":{"name":"2006 Semantics, Knowledge and Grid, Second International Conference on","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the utility of neural network ensembles in knowledge discovery and integration. A novel neural network ensemble model KBNNE (Knowledge-Based Neural Network Ensembles) integrating KDD (Knowledge Discovery in Database) techniques and neural network modeling algorithms by ¿parallel operations¿ is proposed. Through balancing the relative importance of knowledge learned by induction and deduction, KBNNE can avoid the knowledge loss and enhance the "transparency" of neural network models. The effectiveness of the proposed model is demonstrated through computer simulations on simple artificial problems and an actual modeling problem.
本文探讨了神经网络集成在知识发现和集成中的应用。提出了一种基于知识的神经网络集成模型KBNNE (Knowledge- based neural network Ensembles)，该模型通过并行运算将KDD (Knowledge Discovery in Database)技术与神经网络建模算法相结合。通过平衡归纳和演绎所学知识的相对重要性，KBNNE可以避免知识损失，增强神经网络模型的“透明度”。通过对简单人工问题和实际建模问题的计算机仿真，验证了该模型的有效性。
{"title":"Knowledge discovery and integration based on a novel neural network ensemble model","authors":"Yong Wang, Hong-Jie Xing","doi":"10.1109/SKG.2006.59","DOIUrl":"https://doi.org/10.1109/SKG.2006.59","url":null,"abstract":"This article explores the utility of neural network ensembles in knowledge discovery and integration. A novel neural network ensemble model KBNNE (Knowledge-Based Neural Network Ensembles) integrating KDD (Knowledge Discovery in Database) techniques and neural network modeling algorithms by ¿parallel operations¿ is proposed. Through balancing the relative importance of knowledge learned by induction and deduction, KBNNE can avoid the knowledge loss and enhance the \"transparency\" of neural network models. The effectiveness of the proposed model is demonstrated through computer simulations on simple artificial problems and an actual modeling problem.","PeriodicalId":210294,"journal":{"name":"2006 Semantics, Knowledge and Grid, Second International Conference on","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123405062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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