改进规则的可放缩矢量图形地图的查询模型

分析了基于SVG(可放缩矢量图形)格式时态GIS(地理信息系统)领域,尤其是地理元素查询方面的研究现状,针对现有的XML(可扩展标记语言)查询方法进行了分析和对比.在基于SVG语法规则改进研究的基础上,并结合LISAⅡ(layered intersection scan algorithmⅡ,分层交叉扫描算法Ⅱ)查询技术,提出了一种适合改进规则的SVG格式的GIS数据地理元素查询模型及其算法,通过定义关键字查询语法规则表达式,定义关键字查询的"&"和"or"操作规则,实现了组合查询;通过定义实体元素节点,减少了节点数,提高了查询效率.该模型实现了SVG格式地图的地理元素查询,包括对整幅地图的各种组合查询和分图层的各种组合查询,解决了基于SVG格式地图元素查询的技术瓶颈.大量的数据验证表明,该查询模型是有效的.

A new approach to query expansion in information retrieval

To eliminate the mismatch between words of relevant documents and user's query and more seriousnegative effects it has on the performance of information retrieval,a method of query expansion on the ba-sis of new terms co-occurrence representation was put forward by analyzing the process of producingquery.The expansion terms were selected according to their correlation to the whole query.At the sametime,the position information between terms were considered.The experimental result on test retrievalconference(TREC)data collection shows that the method proposed in the paper has made an improve-ment of 5%～19% all the time than the language modeling method without expansion.Compared to thepopular approach of query expansion,pseudo feedback,the precision of the proposed method is competi-tive.

Quick Web Services Lookup Model Based on Hierarchical Registration

Quick Web Services Lookup (Q-WSL) is a new model to registration and lookup of complex services in the Internet. The model is designed to quickly find complex Web services by using hierarchical registration method. The basic concepts of Web services system are introduced and presented, and then the method of hierarchical registration of services is described. In particular, service query document description and service lookup procedure are concentrated, and it addresses how to lookup these services which are registered in the Web services system. Furthermore, an example design and an evaluation of its performance are presented.Specifically, it shows that the using of attributionbased service query document design and contentbased hierarchical registration in Q-WSL allows service requesters to discover needed services more flexibly and rapidly. It is confirmed that Q-WSL is very suitable for Web services system.

Integrated Water and CGE Model of the Impacts of Water Policy on the Beijing's Economy and Output

The article used general equilibrium model to analyze the change of gross domestic product and industry output affected by water resources policies in Beijing City by using GEMPACK soft tool.The article researches on rules of water supply and demand,evaluating water resources,building water resources input and output table,establishing water computable general equilibrium model and stimulating water policy.The stimulation gives a scenario that increases water price by 10%.The result shows the following aspects.First,water resources policy infects gross domestic product and industry output in different ways.There are different behaviors in different industries as to the water policy. Agriculture industry has the same tendency as water price change and it has more sensitive to water quantity than to water price.For basic energy industries such as oil and chemistry and gas,they show diversity tendency.As to some high water consumer industry such as paper and textile etc.,water resource economic policy can infect them greatly and can promote them to accomplish more water-saving technology.Waste water and construction and service industries show the same tendency as to water policy.Second,government should pay more attention to water resource policy by macro economic administration.The simulation also shows that the output and supply and consumer price change more than expect as to water policy in a free market economic in water industry.So as to a government policy maker,one should be more carefully and prepare suitable forecast and plan to water policy and its negative impact.

Probabilistic Top-k Query:Model and Application on Web Traffic Analysis

Top-k ranking of websites according to traffic volume is important for Internet Service Providers(ISPs) to understand network status and optimize network resources. However, the ranking result always has a big deviation with actual rank for the existence of unknown web traffic, which cannot be identified accurately under current techniques. In this paper, we introduce a novel method to approximate the actual rank. This method associates unknown web traffic with websites according to statistical probabilities. Then, we construct a probabilistic top-k query model to rank websites. We conduct several experiments by using real HTTP traffic traces collected from a commercial ISP covering an entire city in northern China. Experimental results show that the proposed techniques can reduce the deviation existing between the ground truth and the ranking results vastly. In addition, we find that the websites providing video service have higher ratio of unknown IP as well as higher ratio of unknown traffic than the websites providing text web page service. Specifically, we find that the top-3 video websites have more than 90% of unknown web traffic. All these findings are helpful for ISPs understanding network status and deploying Content Distributed Network(CDN).

Comparisons of three data storage models in parametric temporal databases

The parametric temporal data model captures a real world entity in a single tuple, which reduces query language complexity. Such a data model, however, is difficult to be implemented on top of conventional databases because of its unfixed attribute sizes. XML is a matured technology and can be an elegant solution for such challenge. Representing data in XML trigger a question about storage efficiency. The goal of this work is to provide a straightforward answer to such a question. To this end, we compare three different storage models for the parametric temporal data model and show that XML is not worse than any other approaches. Furthermore, XML outperforms the other storages under certain conditions. Therefore, our simulation results provide a positive indication that the myth about XML is not true in the parametric temporal data model.

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

The real-time prediction of bearing wear for roller cone bits using the Intelligent Drilling Advisory system （IDAs） may result in better performance in oil and gas drilling operations and reduce total drilling cost. IDAs is a real time engineering software and being developed for the oil and gas industry to enhance the performance of complex drilling processes providing meaningful analysis of drilling operational data. The prediction of bearing wear for roller cone bits is one of the most important engineering modules included into IDAs to analyze the drilling data in real time environment. The Bearing Wear Prediction module in IDAs uses a newly developed wear model considering drilling parameters such as weight on bit （WOB）, revolution per minute （RPM）, diameter of bit and hours drilled as a function of International Association of Drilling Contractors （IADC） bit bearing wear. The drilling engineers can evaluate bearing wear status including cumulative wear of roller cone bit in real time while drilling, using this intelligent system and make a decision on when to pull out the bit in time to avoid bearing failure. The wear prediction module as well as the intelligent system has been successfully tested and verified with field data from different wells drilled in Western Canada. The estimated cumulative wears from the analysis match close with the corresponding field values.

Construction and Application of Oral English Evaluation Index System Based on AHP

The paper confirms evaluation indexes of oral English with the application of Delphi technique and expert consultation, and then establishes hierarchical structure model of oral English evaluation index system. The weight of each evaluation index is determined with the methods of Delphi and expert consultation again. A group of students＇ oral English is subsequently evaluated by using AHP （Analytic Hierarchy Process） based on oral English evaluation index system and the weights we set up in this thesis.

Economic Implications of Avoiding Dangerous Climate Change： An Analysis Using the AIM/CGE [Global] Model

The purpose of this study is to analyze economic impacts of reducing greenhouse gases emissions significantly. A large amount of emissions reductions are required throughout this century to avoid dangerous climate change, and understanding the economic consequences under such situations is important and meaningful. The AIM/CGE [Global] model, a recursive dynamic computable general equilibrium model on a global scale, is applied to analyze carbon prices and changes in GDP when implementing five policy scenarios represented by emissions pathways, respectively. As a result of the analysis, higher carbon prices and larger decreases in GDP compared to the baseline emissions scenario are observed when emissions are reduced more deeply. However, such GDP losses are rather small and insignificant compared to the GDP growth observed throughout the century. These results suggest that although it is challenging to reduce emissions until the level to avoid dangerous climate change, there is a sufficient possibility to achieve it from economic perspectives.

Research on Semantic and Cache Model of Relational Database

The paper firstly analyze cache replacement strategies at present, and proposed the ideas of the semantic query cache replacement based on user access features, and describe the semantic similarity calculation and realize the algorithm of replacement strategy. The strategy use semantic to match information in the query cache, through dynamic analysis and tracking three characteristics of user access time, user access to content and Business Association, give out the similarity minimum of the cache item, to improve the hit ratio of the cache and the response time and throughput of the server is improved.

Image retrieval based on multi-concept detector and semantic correlation

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.