Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and i...Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.展开更多
Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and t...Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.展开更多
文摘Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.
基金The Central College Fund Free Exploration Projects,China(No.14D111002)The Research Achievements of Shanghai Public Crisis of Cross-Border Governance Research Achievements,China(No.15D111001)
文摘Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.
