MapXtreme for Windows是MapInfo公司推出的基于Internet的网络地图应用服务器。本文详细介绍了MapXtreme的B/S结构模型,网络GIS的特点和利用MapXtreme进行网络GIS开发的基础、方法,并运用MapXtreme技术进行了防汛网络GIS开发的实践研究...MapXtreme for Windows是MapInfo公司推出的基于Internet的网络地图应用服务器。本文详细介绍了MapXtreme的B/S结构模型,网络GIS的特点和利用MapXtreme进行网络GIS开发的基础、方法,并运用MapXtreme技术进行了防汛网络GIS开发的实践研究,成功实现了某实验地区防汛图形信息的网上发布、检索和分析,建立了该地区防汛GIS网站。展开更多
DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the alg...DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.展开更多
As a powerful distributed data processing mechanism,MapReduce supports abundant parallel applications that process massive data on computer clusters.To process the massive data efficiently and correctly,a rational des...As a powerful distributed data processing mechanism,MapReduce supports abundant parallel applications that process massive data on computer clusters.To process the massive data efficiently and correctly,a rational design for the MapReduce procedure is desired.An irrational MapReduce procedure can cause great waste of computing resources and even paralyze the execution system.With the wide application of MapReduce,the unavoidable drawback of irrational MapReduce procedures becomes increasingly serious.To solve this problem,a method for verifying the rationality of a MapReduce procedure before executing it on a computer cluster is proposed.This method constructs the rationality criteria for MapReduce,and then studies an automatic approach for modelling MapReduce with an executable model object Petri net(OPN).Finally,the approaches for automatically identifying the rationality criteria by analyzing the consequence of model execution is developed.The results from extensive case studies demonstrate that the proposed method is feasible and effective.展开更多
文摘MapXtreme for Windows是MapInfo公司推出的基于Internet的网络地图应用服务器。本文详细介绍了MapXtreme的B/S结构模型,网络GIS的特点和利用MapXtreme进行网络GIS开发的基础、方法,并运用MapXtreme技术进行了防汛网络GIS开发的实践研究,成功实现了某实验地区防汛图形信息的网上发布、检索和分析,建立了该地区防汛GIS网站。
基金Project(61103046) supported in part by the National Natural Science Foundation of ChinaProject(B201312) supported by DHU Distinguished Young Professor Program,China+1 种基金Project(LY14F020007) supported by Zhejiang Provincial Natural Science Funds of ChinaProject(2014A610072) supported by the Natural Science Foundation of Ningbo City,China
文摘DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.
基金supported by the Natural Science Foundation of Hubei Province,China(2016CFB287)
文摘As a powerful distributed data processing mechanism,MapReduce supports abundant parallel applications that process massive data on computer clusters.To process the massive data efficiently and correctly,a rational design for the MapReduce procedure is desired.An irrational MapReduce procedure can cause great waste of computing resources and even paralyze the execution system.With the wide application of MapReduce,the unavoidable drawback of irrational MapReduce procedures becomes increasingly serious.To solve this problem,a method for verifying the rationality of a MapReduce procedure before executing it on a computer cluster is proposed.This method constructs the rationality criteria for MapReduce,and then studies an automatic approach for modelling MapReduce with an executable model object Petri net(OPN).Finally,the approaches for automatically identifying the rationality criteria by analyzing the consequence of model execution is developed.The results from extensive case studies demonstrate that the proposed method is feasible and effective.
