The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time serie...The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time series,including Euclidean distance,Manhattan distance,and dynamic time warping(DTW).In contrast,DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series.However,due to its quadratic time and space complexity,DTW is not suitable for large time series datasets.Many improving algorithms have been proposed for DTW search in large databases,such as approximate search or exact indexed search.Unlike the previous modified algorithm,this paper presents a novel parallel scheme for fast similarity search based on DTW,which is called MRDTW(MapRedcuebased DTW).The experimental results show that our approach not only retained the original accuracy as DTW,but also greatly improved the efficiency of similarity measure in large time series.展开更多
本文提出了一种用于技术融合与演化路径探测的新方法,即技术群相似度时序分析法,并将其应用于增材制造领域的技术发展路径分析。首先,以增材制造技术专利数据为分析对象,从专利文献记录层面、技术层面和技术域层面,依次对该技术领域整...本文提出了一种用于技术融合与演化路径探测的新方法,即技术群相似度时序分析法,并将其应用于增材制造领域的技术发展路径分析。首先,以增材制造技术专利数据为分析对象,从专利文献记录层面、技术层面和技术域层面,依次对该技术领域整体变化趋势进行测度;再次,基于IPC (International Patent Classification)分类号共现原理,利用社区探测算法识别技术群,并通过余弦相似度关联相邻时间区间的技术群;最后,通过可视化技术展示不同时间区间内技术群之间的融合和扩散演化关系。研究结果表明,增材制造技术处于快速发展期,技术融合能力与继承性逐渐增强,日益成为相对独立的技术领域。增材制造技术融合与扩散演化路径明确,主要包含增材制造材料和工艺、计算机辅助设计和增材制造应用三大主路径。近年来,金属增材制造和电弧增材制造成为技术热点,生物医疗领域、建筑领域和食品领域成为技术重点应用领域。技术群相似度时序分析法是传统IPC共现方法的有益补充,从动态视角展示技术演化路径,为全面探测技术演化路径提供新的视角和技术手段。展开更多
在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考...在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考虑到节点间未来一段时间内的最大相遇概率以及对应的相遇时间,结合分簇结果和相遇情况生成动态路由表,完成一种单副本的路由方法.该方法首先依据节点间路径的相似程度进行分簇,然后运用半马尔可夫模型预测节点间未来某一时刻的相遇概率,依据源节点和目的节点所在的分簇确定可以应用到路由中的节点集合,最后根据路径搜索策略找到最优路径,生成与当前时刻有关的动态路由表.仿真结果表明CRSMP在缓存较小的情况下投递成功率远高于DirectDeliveryRouter、FirstContactRouter和SimBetRouter三种单副本路由方式以及Spray and Wait、Epidemic和Prophet三种多副本路由协议.在10M缓存下的CRSMP有着与500M缓存下的Epidemic相近的路由性能.进一步在真实数据集上进行测试,测试结果表明CRSMP算法依然有着较好的路由性能.展开更多
基金supported in part by National High-tech R&D Program of China under Grants No.2012AA012600,2011AA010702,2012AA01A401,2012AA01A402National Natural Science Foundation of China under Grant No.60933005+1 种基金National Science and Technology Ministry of China under Grant No.2012BAH38B04National 242 Information Security of China under Grant No.2011A010
文摘The similarity search is one of the fundamental components in time series data mining,e.g.clustering,classification,association rules mining.Many methods have been proposed to measure the similarity between time series,including Euclidean distance,Manhattan distance,and dynamic time warping(DTW).In contrast,DTW has been suggested to allow more robust similarity measure and be able to find the optimal alignment in time series.However,due to its quadratic time and space complexity,DTW is not suitable for large time series datasets.Many improving algorithms have been proposed for DTW search in large databases,such as approximate search or exact indexed search.Unlike the previous modified algorithm,this paper presents a novel parallel scheme for fast similarity search based on DTW,which is called MRDTW(MapRedcuebased DTW).The experimental results show that our approach not only retained the original accuracy as DTW,but also greatly improved the efficiency of similarity measure in large time series.
文摘本文提出了一种用于技术融合与演化路径探测的新方法,即技术群相似度时序分析法,并将其应用于增材制造领域的技术发展路径分析。首先,以增材制造技术专利数据为分析对象,从专利文献记录层面、技术层面和技术域层面,依次对该技术领域整体变化趋势进行测度;再次,基于IPC (International Patent Classification)分类号共现原理,利用社区探测算法识别技术群,并通过余弦相似度关联相邻时间区间的技术群;最后,通过可视化技术展示不同时间区间内技术群之间的融合和扩散演化关系。研究结果表明,增材制造技术处于快速发展期,技术融合能力与继承性逐渐增强,日益成为相对独立的技术领域。增材制造技术融合与扩散演化路径明确,主要包含增材制造材料和工艺、计算机辅助设计和增材制造应用三大主路径。近年来,金属增材制造和电弧增材制造成为技术热点,生物医疗领域、建筑领域和食品领域成为技术重点应用领域。技术群相似度时序分析法是传统IPC共现方法的有益补充,从动态视角展示技术演化路径,为全面探测技术演化路径提供新的视角和技术手段。
文摘在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考虑到节点间未来一段时间内的最大相遇概率以及对应的相遇时间,结合分簇结果和相遇情况生成动态路由表,完成一种单副本的路由方法.该方法首先依据节点间路径的相似程度进行分簇,然后运用半马尔可夫模型预测节点间未来某一时刻的相遇概率,依据源节点和目的节点所在的分簇确定可以应用到路由中的节点集合,最后根据路径搜索策略找到最优路径,生成与当前时刻有关的动态路由表.仿真结果表明CRSMP在缓存较小的情况下投递成功率远高于DirectDeliveryRouter、FirstContactRouter和SimBetRouter三种单副本路由方式以及Spray and Wait、Epidemic和Prophet三种多副本路由协议.在10M缓存下的CRSMP有着与500M缓存下的Epidemic相近的路由性能.进一步在真实数据集上进行测试,测试结果表明CRSMP算法依然有着较好的路由性能.
