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.     

测试(试井)成果影响因素浅析

随着测试技术的发展、测试质量及资料解释水平的提高，测试（试井）成果的可靠程度越来越受到重视。影响解释成果的环节和因素很多，从客观和主观两方面分析其主要因素，找出症结所在，从而完善现场环节，完善资料解释过程，提高测试技术及解释水平。     

测井资料在优选压裂层位中的应用

压裂是低孔、低渗油层改造的主要增产措施。根据常规测井资料、岩石物理实验分析数据及试油成果。研究储层四性关系，确定区块的低孔、低渗储层评价标准。进一步对油层进行分类，有针对性地为油层压裂提供施工层位和准确的储层参数。通过商543区块实例，阐述了选择压裂层位的方法。     

IDS告警融合与关联模型的研究

入侵检测是保障网络安全的重要手段,针对现有入侵检测系统中告警数量多、协调性差等问题,论文提出了一种具有告警融合与关联功能的告警处理系统模型,该模型冗余告警量少、整体检测能力强,并能进行攻击企图的预测,能有效提高入侵检测的效率,有助于进一步增强网络的健壮性。     

基于Delphi嵌入汇编数据采集程序的设计

实现嵌入式汇编技术就是直接把汇编语言的代码写到高级语言的代码中并一起进行编译,不需要独立的汇编系统和另外的连接步骤。嵌入式汇编技术在不同的高级语言中语法各不相同。文章主要给出了在Delphi环境中嵌入汇编的具体用法。采用嵌入式汇编技术对Intel8255和8254接口芯片的具体操作,以及介绍了数据采集软件的设计流程和程序的具体实现方法。     

GIS在深圳市三防指挥信息系统中的应用

深圳市三防指挥信息系统利用GIS技术,结合水利(水务)、防汛、抗旱的具体业务,建立了水情遥测数据处 理分系统、工况监测数据处理分系统、水务行业三防信息管理分系统、决策支持分系统、水源优化调度分 系统,提高了现代化管理水平。     

泥页岩井壁稳定研究及在临盘地区的应用

从岩石力学和物理化学两个主要因素简要分析了井壁失稳的机理，指出泥页岩井壁失稳是由力学与化学两方面因素共同作用的结果。钻井液与泥页岩存在化学势差，并改变了井壁附近的孔隙压力，降低岩石强度。借助于井壁处有效应力的变化,将泥页岩与钻井液相互作用时页岩水化所产生的力学效应与纯力学效应结合起来，计算出任意井斜方位井眼围岩应力状态，利用测井资料求出有关岩石力学参数和Mohr Couloumb准则，计算得出防塌的临界钻井液密度。     

某面板堆石坝沉降观测资料反馈分析

根据某面板堆石坝施工期、蓄水初期以及几年来的沉降观测资料，对坝体填筑材料计算参数进行反馈分析，得出了能够反映坝体实际变形的弹塑性应力变形计算参数，有利于今后建立大坝变形预报模型，更准确地把握大坝实时运行状态。     

从数据库到数据仓库——广电未来的制胜武器

在目前数据爆炸、信息匮乏的情况下，需要利用数据仓库系统集成1个或多个独立传统数据库数据，通过分析海量数据发现市场潜在规律，获取商业信息以支持商业决策，这将是广电未来的制胜武器。     

Effective Location Based Services with Dynamic Data Management in Mobile Environments

With the proliferation of mobile computing technologies, location based services have been identified as one of the most promising target application. We classify mobile information service domains based on feature characteristics of the information sources and different patterns of mobile information access. By carefully examining the service requirements, we identify the dynamic data management problem that must be addressed for effective location based services in mobile environments. We then devise a general architecture and cost model for servicing both location independent and location dependent data. Based on the architecture and cost model, we propose a set of dynamic data management strategies that employs judicious caching, proactive server pushing and neighborhood replication to reduce service cost and improve response time under changing user mobility and access patterns. Detail behavior analysis helps us in precisely capturing when and how to apply these strategies. Simulation results suggest that different strategies are effective for different types of data in response to different patterns of movement and information access. Shiow-yang Wu is an associate professor of the Department of Computer Science and Information Engineering at National Dong Hwa University, Hualien, Taiwan, R.O.C. He received the BS and MS degrees in computer engineering from National Chiao Tung University, Hsinchu, Taiwan, ROC, and the PhD degree in computer science from the University of Texas at Austin in 1984, 1986, and 1995, respectively. His research interests include data/knowledge bases, mobile computing, distributed processing, intelligence information systems, and electronic commerce. Kun-Ta Wu was born in Taipei, Taiwan, R.O.C., in 1976. He received the B.S. degree in computer science from Soochow University, Taipei, Taiwan, R.O.C., in 1999 and the M.S. degree in computer science and information engineering from National Dong Hwa University, Hualien, Taiwan, R.O.C., in 2001. Currently, he is an Assistant Researcher in the Domestic Division at Science and Technology Information Center, National Science Council, R.O.C., as a member of Information Gathering and Analysis Group of National Information and Communication Security Taskforce. His research interests include mobile computing, wireless network and information security.