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.     

基于mobile agent的分布式网络自管理模型

为了解决大规模、异构网络环境下基于“管理员密集”的传统网络管理模式带来的低效率和高出错率问题，提出了一种网络自管理模型，从网络管理的自动初始化和动态自管理两个方面实现对网络自动、自主、自适应的管理；提出了管理定位服务这一关键技术，在多个管理站协同工作的分布式网络环境中为设备自动定位出合适的管理站及初始的管理任务；采用可伸缩的分布式体系结构，适应了大规模网络的规模可变性等特点；基于mobile agent的管理框架进一步提高了管理的自适应性和灵活性。     

能源实时监控与信息管理系统出现的问题及解决的方法

中国石化北京燕化石油化工股份有限公司动力事业部二供水车间电气改造完成后，能源实时监控与信息管理系统出现线路名称不统一、电流互感系数变比不起作用、数据查询功能及统计报表数据的准确性等问题，严重地影响系统的使用。通过对比分析，找到了出现问题的实质，对源代码进行修改，问题得到了解决。同时也成功地解决了其他部门遇到相类似的问题。     

管道风险管理方法研究

按照管道风险管理的流程分别对管道风险评价、风险控制和决策支持、效能测试和响应进行了论述。针对目前国内管道行业的情况，提出了进行管道风险评价的有效方法及维护措施。着重介绍了国外管道风险可接受标准的情况，作为国内制定管道风险评价标准的参考。     

谈国际石油资源的利用

全球经济的迅猛发展使能源问题日益受到关注,而石油资源的紧缺已成为当前能源利用的首要问题,世界各国企业都加入了激烈的石油资源的竞争当中。根据目前世界石油资源的现状和中国石油资源的供需形势,结合石化企业的特点,对如何更好地利用国际石油资源提出了对策和建议。     

论四川盆地天然气勘探前景

中石油及其前身已在四川盆地从事天然气勘探开发工作50余年,截至2005年底累计探明天然气地质储量逾8400×10⁸m³,累计生产天然气超过2300×10⁸m³,年产量现已超过120×10⁸m³,已成为国内目前最大的产气区。对于该盆地进一步勘探前景如何的命题,从以下4个方面给予了论证：一是资源丰富、勘探成果持久不衰，近期仍有大发现；二是要辩证地认识盆地油气地质的复杂性，探索新规律，解放新领域；三是寻求地质认识和勘探技术进步是勘探突破的关键；四是继续以盆地两大地质体系（海相古隆起体系及陆相前陆盆地体系）和8套裂缝-孔隙性储集层（中上寒武统白云岩、石炭系白云岩、上二叠统长兴组生物礁白云岩、下三叠统飞仙关组鲕状白云岩、下三叠统嘉陵江组粒屑白云岩、中三叠统雷口坡组藻粒白云岩、上三叠统须家河组长石岩屑石英砂岩和中上侏罗统红层砂岩）为对象获取更多的天然气资源量及优质储量。据此得出结论：四川盆地天然气勘探前景是广阔的，但勘探难度会逐步加大。     

水资源系统分析法在水资源优化配置中的应用

探讨了水资源系统分析法的步骤和数学模型,并以线性规划模型为例建立了水资源优化配置的数学模型,对水资源优化配置提供了一定的依据。     

农村饮水解困工程持续利用的探讨

分析了尧都区农村饮水工程存在的问题，阐述了农村饮水解困工程持续利用的管理措施，提出建立新的农村供水市场理念。     

Efficient multicast search under delay and bandwidth constraints

The issue of a multicast search for a group of users is discussed in this study. Given the condition that the search is over only after all the users in the group are found, this problem is called the Conference Call Search (CCS) problem. The goal is to design efficient CCS strategies under delay and bandwidth constraints. While the problem of tracking a single user has been addressed by many studies, to the best of our knowledge, this study is one of the first attempts to reduce the search cost for multiple users. Moreover, as oppose to the single user tracking, for which one can always reduce the expected search delay by increasing the expected search cost, for a multicast search the dependency between the delay and the search cost is more complicated, as demonstrated in this study. We identify the key factors affecting the search efficiency, and the dependency between them and the search delay. Our analysis shows that under tight bandwidth constraints, the CCS problem is NP-hard. We therefore propose a search method that is not optimal, but has a low computational complexity. In addition, the proposed strategy yields a low search delay as well as a low search cost. The performance of the proposed search strategy is superior to the implementation of an optimal single user search on a group of users. Amotz Bar-Noy received the B.Sc. degree in 1981 in Mathematics and Computer Science and the Ph.D. degree in 1987 in Computer Science, both from the Hebrew University, Israel. From October 1987 to September 1989 he was a post-doc fellow in Stanford University, California. From October 1989 to August 1996 he was a Research Staff Member with IBM T. J. Watson Research Center, New York. From February 1995 to September 2001 he was an associate Professor with the Electrical Engineering-Systems department of Tel Aviv University, Israel. From September 1999 to December 2001 he was with AT research labs in New Jersey. Since February 2002 he is a Professor with the Computer and Information Science Department of Brooklyn College - CUNY, Brooklyn New York. Zohar Naor received the Ph.D. degree in Computer Science from Tel Aviv University, Tel Aviv, Israel, in 2000. Since 2003 he is with the University of Haifa, Israel. His areas of interests include wireless networks, resource management of computer networks, mobility, search strategies, and multiple access protocols.     

分类挖掘技术在高校人事管理中的应用

探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型，应用数据挖掘中决策树算法，对高校人力资源数据源中的信息进行分析，发现其中有价值的数据模式，寻找其中存在的关系和规则，对高校人才规划提供比较客观的决策支持。