一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

新形势下煤矿企业外包工程安全管控模式研究

针对煤矿企业外包工程安全生产问题严重、事故多发的特点，通过对新的煤矿企业外包工程的安全管控模式研究，分析煤矿安全监管、技术和装备水平、安全培训等存在的问题，提出了实施煤矿外包工程队准入制度，创新外包安全管控模式，找到对外包工程安全状况进行管理的行之有效的办法。     

高频次微小地震下顺倾软硬互层边坡动力稳定性研究

三峡库区(TGR)自蓄水以来微震活动加剧、强度增大,频发微震会对库区边坡产生一定影响。采用振动台模型试验和UDEC数值模拟方法,对库区顺倾软硬互层边坡在不断微、小地震作用下的破坏失稳演化过程及动力稳定性进行了研究。研究成果如下:在不断地震作用后,边坡自振频率下降、阻尼比上升,自振频率下降的速度随加载次数和加载幅值的增加而增大;在不同加载阶段,边坡坡面PGA放大系数不断降低,动力响应呈现减弱趋势;弱层成为顺倾软硬互层边坡变形破坏的优势区域;边坡的破坏失稳演化过程为分段式的滑移破坏过程,上部软、硬层滑落后,剩余滑体沿着由上部弱层剪切裂缝、中部硬层次级节理拉裂缝和下部弱层剪切裂缝贯通形成的滑移面滑移破坏;UDEC数值模拟表明顺倾软硬互层边坡的累积永久位移随微震作用次数的增加而增加,稳定性系数则呈现递减趋势。研究成果对库区滑坡形成机制的认识和减灾、防灾有一定的价值。     

弹簧限位抗震支座在五棵松文化体育中心体育馆大跨钢屋架设计中的应用

五棵松文化体育中心体育馆为2008北京奥运会篮球馆,钢结构屋架跨度为122.3m,采用了非等高(6.3~9.3m)双向正交钢桁架体系。桁架结构支承在周边20根钢筋混凝土柱上。除与四角柱为双向固定铰接外,钢屋架与其它柱在平行于桁架方向采用了有弹簧限位的滑动支座,垂直于桁架方向为固定铰接。滑动支座有效地控制了屋架传给承重柱的推力,尤其在地震作用下。在考虑竖向荷载组合、多遇地震荷载组合和设防地震荷载组合的情况下,均能满足承载力极限状态和正常使用极限状态要求。     

NPC三电平逆变器最优化虚拟电压矢量控制

三电平逆变器的中点电位不平衡不仅使开关管的电压应力增加,还会导致输出电流的谐波畸变率增高。因此,三电平逆变器的中点电位平衡控制一直是国内外学者的研究热点。以中点钳位型(NPC)三电平逆变器作为研究对象,提出了一种最优化虚拟电压矢量中点电位平衡控制方法。该方法首先以三电平逆变器固有的有限个离散电压矢量为基础,构造出连续的虚拟电压矢量。然后以参考电压和中点电位平衡为控制目标定义代价函数,此代价函数可以灵活方便地控制逆变器的综合性能。由最优化方法求解出使代价函数值最小的虚拟电压矢量,即为控制参考电压和中点电位平衡的全局最优解,此方法不仅可以保证输出电压有较好的跟踪效果,还具有较强的中点电位平衡能力和抗干扰能力。仿真和实验都对此进行了有效的证明。     

系统论在故障定位中的应用

营业厅、呼叫中心终端IE浏览器频繁出现“请求出错”、“获取信息失败”及白屏现象如何解决？怎样定位问题原因？本文运用系统论的理念,通过tcpdump、wireshark等工具捕获并深入到TCP、HTTP协议级别分析关键环节网络数据包,精确定位ArrayNETWORKS厂商的负载均衡设备bug,根本解决了营业厅、呼叫中心终端IE浏览器的异常问题。     

大数据中效用挖掘的快速单阶段算法

现有数据挖掘算法的缺点是在挖掘大数据时会出现大量候选模式,从而造成可伸缩性瓶颈,个别算法虽然不生成候选模式,但是计算代价高昂,缺乏有效剪裁,运行效率存在瓶颈.为此,提出一个全新的单阶段不生成候选模式的数据挖掘算法,其创新性有3点:一是基于前缀生长的模式枚举和基于效用上限值评估的剪裁策略;二是基于稀疏矩阵和虚拟投影的效用信息表达;三是节省存储空间的深度优先搜索方法.大量实验表明,新算法的时间效率比现有算法高5倍以上,并且内存使用量比现有算法少20％～60％,可伸缩性高.     

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.     

Twitter data analysis by means of Strong Flipping Generalized Itemsets

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis of Twitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets – sets of items at different abstraction levels – can be effectively mined and used to discover interesting multiple-level correlations among data supplied with taxonomies. Each generalized itemset is characterized by a correlation type (positive, negative, or null) according to the strength of the correlation among its items.This paper presents a novel data mining approach to supporting different and interesting targeted analysis – topic trend analysis, context-aware service profiling – by analyzing Twitter posts. We aim at discovering contrasting situations by means of generalized itemsets. Specifically, we focus on comparing itemsets discovered at different abstraction levels and we select large subsets of specific (descendant) itemsets that show correlation type changes with respect to their common ancestor. To this aim, a novel kind of pattern, namely the Strong Flipping Generalized Itemset (SFGI), is extracted from Twitter messages and contextual information supplied with taxonomy hierarchies. Each SFGI consists of a frequent generalized itemset X and the set of its descendants showing a correlation type change with respect to X.Experiments performed on both real and synthetic datasets demonstrate the effectiveness of the proposed approach in discovering interesting and hidden knowledge from Twitter data.