Earticle

With the rapid development of cloud computing technology, it’s the cloud rendering that cloud computing is applied to render the job in CG (Computer Graphic) industry. The cloud rendering can handle a large number of rendering requests which are enormous pressure for back-end servers in system. Facing with massive data and computing resources, the bottleneck of original HDFS (Hadoop Distributed File System) based on cloud computing has become more and more prominent, such as the failure of single Namenode, scalability issues. Therefore, the paper proposed an improved HDFS which evolved a single Namenode into multi-Namenode. In HDFS, Metadata management is very important. So this paper presented a two-level Metadata distribution algorithm. The two-level algorithm was based on the principle that different distribution strategies were used to different categories of Metadata. The experiments verified that the improved HDFS effectively improved the performance of the system.

Standardizing the various benefits and performance features of the different government R&D programs is difficult. This is largely because each of them involves a wide variety of necessary research. In order to minimize benefit distinctions—the difference in benefit between the proposal and the alternative--the OECD benefit assessment report was examined. Associated Research results and benefit distinctions from preliminary feasibility data were also used to draw benefit estimation hindrance factors. Analytic Hierarchy Process is used to identify the relative importance rank of benefit estimation hindrance factors. If Independence between benefit estimation hindrance factors fails to satisfy the evaluation criteria then, a model based on the fuzzy measure is applied. This is for drawing optimal evaluation results, In order to know the correlation between benefit distinctions and benefit estimation hindrance factors ordered digit model is utilized. The application of big data technique is used as a means to collect extensive trend data and adequately capture technology trends. In this paper, the R&D program related to Information Technology was classified into four categories (First-mover, Catch-up, Data existence). Finally a methodology for extracting a relevant market scale and a market share data is proposed.

As traditional Linear Discriminant Analysis algorithm runs slowly in large data set, this paper proposed a fast LDA algorithm based on active learning. In the proposed algorithm, the original training set is divided into three parts, i.e. initial training set, correction set and testing set. Secondly, LDA algorithm is running on the initial training set, and the projection vector can be obtained. Thirdly, we select from correction set the samples whose projection is farthest from the mean vector, add them into the initial training set and compute the projection vector again. Repeat this step until the classification precision attains the expected target or the correction set is empty. The simulation experiments on the UCI data set and the MNIST dataset show that the proposed algorithm running fast on large data set, and has a good classification precision.

With the rapid development of Internet and information technologies, E-business is rapidly becoming the focus of business activity. However the traditional E-business application system exists the non-unitary technical standards, lack of unified commercial release mechanism, difficult information exchange and cooperation, long development time, difficult reconstruction and upgrading maintenance. A large number of Web services, JAVA EE technology has become more mature and stable. So the key techniques of XML, SOAP, WSDL and UDDI in Web service are analyzed in detail, then a new E-business application framework based on Java EE and Web services is proposed to overcome the shortcomings of the traditional E-business application system. In the this E-business application framework, the characteristics and architecture of Web service are used to realize the standard and loosely coupled application architecture and guarantee compatible information exchange and cooperation. The Java EE framework is use to ensure the more strong security and better stability for E-business application framework. So the new E-business application framework takes on crossing platform, flexibility and easy expansion, and can meet the openness, complexity, distribution, dynamic and customization of E-business.

Applying to standard clinical terminologies is essential for understanding the precise meanings of the clinical terminology used in EMR systems and sharing clinical data among health providers. In Korea, Korean standard terminology of medicine was first introduced as national standard vocabulary for EMR systems in 2014. However, there is little usage yet. So we studied on a comparison of diagnosis domain between Korean standard terminology of medicine and SNOMED CT adapted in many countries. Because it is the most important in medical statistics and clinical studies. Qualitative analysis, quantitative analysis and mapping ability were studied through literature review and structure analysis as methods. As a result, Korean standard terminology of medicine was satisfied in the concept orientation, concept permanence, non- semantic concept identifier and mapping to standards terminologies, support for multiple level. But it was not in the multi-hierarchy, language independence, formal definition and so on. And some problems was raveled in the structural aspect and mapping. This paper will help to utilize and improve KOSTOM.

CHI statistical text feature selection method based on information entropy optimization is presented in this paper. In the text categorization process of feature selection, considering the results of effect of the distribution within categories and among categories, we introduce the frequency of features information entropy among categories, the information entropy within categories, information within category to optimize the CHI statistical methods. The experimental results show that the classification accuracy of the optimized CHI method is significantly higher than that the traditional CHI statistical methods.

The analysis of social networking sites is a vast area of research as there are tremendous measures of records showing up in online networking. Predicting the comment patterns of users on these sites is a complex decision making process. This paper proposes a hybrid model of linear regression (PACE regression) and non linear regression (REP Tree) that predicts the likelihood of the comment volume, which a post may receive by analyzing the various features of the corresponding page, post and previous records of comment patterns of users. To mechanize the procedure, a model is built comprising of the crawler, data processor and information revelation module. The new hybridized model has improved the time and space complexity along with Accuracy by building a right sized tree using only significant features with low misclassification rate.

Most existing data publish/subscribe systems applied in a particular field, the lack of generality. In order to satisfy the general support for interdisciplinary model, proposed an improved model of general data publish/subscribe. The model supports the configuration and modification of the underlying data types. In order to avoid the impact on the application layer while changing the underlying DDS product, proposed an encapsulation on DDS based on the abstract factory pattern. Finally, through simulation experiments to verify the feasibility of the proposed model, the simulation results show that the improved model can be well applied to various types of data publish/subscribe occasions, with high performance.

Open network knowledge base has become an effective tool and it provides a new resource and method for solving the problem in web information retrieval. The integration of information technology and curriculum based on the network environment, especially the multimedia technology, is able to create an ideal learning environment for autonomous learning. In this paper, the author analyzes the open network knowledge and independent inquiry teaching mode of college Chinese course. Through the inquiry teaching experiment, the result shows that the score in experimental class is higher than the control group, and the statistical result is significant. So that, the inquiry teaching mode of college Chinese course is effective, at the same time, it could also increase the students' learning interest.

This study is to figure out Influence factors on Admission costs, based on In-Patient Cost Analysis. The study reviewed German Cost Analysis Method, drew Cost analysis Method applicable to Korean hospitals, and then conducted Analysis of Variance (ANOVA) and Multiple Regression, to analyze what factors influenced Admission Costs. The empirical analysis has shown that the cost recovery level was influenced by ‘Hospital visit days’, ‘Examination costs’ and ‘Physician costs’ Especially the cost recovery level had a positive relationship with Examination cost rate and a negative relationship with Physician cost rate, which directly affected surgical units and medical units thus caused the imbalance of Cost recovery level between both units. In other words, surgical units cost recovery level was low, however medical units was relatively high. Therefore the improvement of Cost recovery system, based on the exact cost information is needed to solve the imbalance of cost recovery level.

Big Data consists of large-volume, complex, growing data sets with multiple, heterogenous sources. With the tremendous development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. In the era of vigorous development network media, rumors propagation procedure becomes more complex, faster, and more dangerous. The identification and control of Internet rumors is important to related net-check departments. This paper establish an index system to evaluate the level of internet rumors and rumor credibility index system according to characteristics of rumor propagation, and analysis the advantages and disadvantages of the models through experimental verification.

Swing door trending (SDT) algorithm is a lossy compression algorithm that be applied on the real-time database and proposed by OSI software company of American; SDT is widely used to compress process data generated by process industry. Using straight line for a data section to linear fitting in traditional SDT algorithm. However, the data generated in the process of industrial production are slightly fluctuating with time. So, the use of linear fitting will lead to a large decompression error. In order to overcome the large decompression error generated by the traditional SDT, we proposed Controllable Curve Fitting Based Swing Door Trending (CCFSDT). The CCFSDT algorithm uses curve line for a data section to fitting, the data restored are closer to the true value. And in order to reduce the cost of curve fitting, it can be appropriate to reduce the total number of points of curve fitting. We filter noise point before fitting to void the impact on the reduction data and achieve better compression effect. The experimental results on simulated data and actual plant data show that: under the same conditions, the CCFSDT can well reduce the errors of decompression and achieve satisfactory performance.

Now a day’s data out sourcing is the main focusing term in real time cloud computing applications. Secure data outsourcing is another real time intellectual concept in cloud computing applications for proceeding efficient data transmission. Conventionally Attribute Based Encryption (ABE) performs efficient data security of data outsourcing in cloud. It performs effective data security based on attributes of uploaded data for storage. Attributes are key terms for converting plain file data to Meta (cipher) file, so every time attribute extraction is complexity in data storage in cloud for efficient security analysis. We describe new public cryptographic system which effects fixed size for efficient delegation of decryptions for cipher-texts. So in this paper we propose to KAE (Key Aggregate Encryption) for efficient data security for providing. The novelty is one can aggregate any set of secret keys and make them as complete with single key with power of all the keys been aggregated. We provide security analysis as a development in real time cloud applications for processing access control data delivery between users present in cloud. Our experimental results show efficient security with access control policies in data storage in cloud.

The development of information technology changes people's consumption patterns, more and more people are starting to accept online shopping, because E-commerce platform has the advantages of convenience, low price and security. In this paper, the author analyze how e-commerce application will effect on small and medium enterprises, by using the data mining method, result shows that e-commerce platform has significant performance in increasing customer and enhancing the brand awareness, more than 77.67% companies agree that application of e-commerce platform can immediately increase orders. The result of factor analysis shows that information quality and service quality is the key factors that will influence the network platform. Therefore, enterprises should strengthen the application of electronic commerce, at the same time; they should also pay attention to the quality of their own network platform.

With the rapid development of big data, cloud computing, the size of the computer processing data is huge. Data mining is the process of revealing a new relationship, trend and pattern by a careful analysis of a large number of data. In this paper, the author analyzes data mining algorithm and the effectiveness of mathematics classroom teaching based on support vector machine. Through data analysis, the results show that teachers are more inclined to teach and ask questions, while students prefer to explore cooperative learning methods. In the process of classroom teaching, teachers should arouse students' enthusiasm and initiative, and further improve the efficiency of classroom teaching.

Big data and hadoop technologies are used for biomedical and health-care informatics research purpose. Huge information advances are progressively utilized for biomedical and medicinal services informatics research. A lot of natural and clinical information have been produced and gathered at a remarkable speed and scale. For instance, the new era of sequencing innovations empowers the expert cessing of billions of DNA grouping information every day, and the use of electronic health records (EHRs) is reporting a lot of patient information. The expense of securing and examining biomedical information is relied upon to diminish drastically with the assistance of innovation overhauls, for example, the rise of new sequencing machines, the improvement of novel equipment and programming for parallel processing, and the broad extension of EHRs. Huge information applications show new chances to find new learning and make novel techniques to enhance the nature of human services. The utilization of enormous information in social insurance is a quickly developing field, with numerous new disclosures and procedures distributed in the most recent five years. In this paper we present different areas of biomedical fields like bioinformatics, clinical informatics, imaging informatics, and general health care informatics. In particular, in bioinformatics, high-throughput tests encourage the exploration of new expansive affiliation investigations of ailments, and with clinical informatics, the clinical field profits by the boundless measure of gathered patient information for settling on savvy choices. Imaging informatics is presently all the more quickly incorporated with cloud stages to share therapeutic picture information and work processes, and general wellbeing informatics influences huge information methods for foreseeing and observing irresistible illness flare-ups, for example, Ebola. In this paper, we survey the late advance and leaps forward of enormous information applications in these human services areas and compress the difficulties, holes, and chances to enhance and progress huge information applications in social insurance.

With the rapid development of modern information technology, especially the development of the Internet, network brings a vast amount of data and information. The development of information technology has changed the education and learning methods, greatly improving our work and study efficiency. In this paper, the author analyzes the college wushu teaching reform based on multimedia technology. Based on multimedia platform, this paper constructs the martial arts teaching activities under the background of modern technology. This system realizes the combination of multimedia technology and the martial arts curriculum structure, content, and resources; it changes the traditional classroom teaching mode, and will improve the students' enthusiasm in wushu teaching.

With the rapid development of the Internet and the continuous expanding of the data network, little potential anomaly can seriously affect the normal operation of the network, and even lead to huge economic losses. In order to be more accurate and efficient in the traffic detection, in this paper, we propose an N-ARMA based traffic anomaly detection model. We also conduct extensive experiments to verify the higher accurate ratio and recall ratio of our model by comparing with other traffic anomaly detection methods.

This paper describes a stemming and lemmatization approach for Uyghur using Conditional Random Fields (CRFs). In the proposed approach, we used syllable-level training and test corpus with the combination of some automatically tagged positional and morphological feature tags. The training and test corpus has been manually tagged with a stemming tag set which includes eight kinds of tags which fully reflect the morphological feature of Uyghur word. It has been observed that some morphological features are very helpful for improving the evaluating results. The syllable-level Precision, Recall and F-score of the best evaluation result respectively are 98.79%,98.71% and 98.75% respectively, and the word-level accuracy we achieved is 95.9%.The experimental results show that the efficiency of this approach is very ideal.

With the rapid development of higher education, how to safeguard and promote the quality of degree training has increasingly become the focus of all sectors of society and training units. Strengthening evaluation is an important process to ensure the quality of the degree-granting. To weaken the human factor and reduce the complexity of human intervention in the evaluation process, this paper presents a new degree evaluation model. This model consists of a command management unit, data unit, sampling rules unit, index system unit, evaluation system unit and information feedback unit. In this model, data cleaning and data integration are used to deal with multi-source heterogeneous degree data, and the rule sampling method is applied to achieve the complex and diverse sampling requirements. To prove the scientific and effective nature of this evaluation model, we applied this model to a sampling of master's dissertations from Shanghai in 2014. The result of using this evaluation model on this sampling met the requirement of the Municipal Degree Committee.

Representation and analysis of the complex spatial network relations are of great significance in fields such as Geographic Information System，spatial database, spatial data mining and intelligent inference. As the existing research achievements cannot be used to deal with the dynamic complex spatial network relations effectively in reality, in order to make up for the deficiency of existing methods, the dynamic complex spatial network relations is studied in detail. The predicate representation methods of the strict and extended complex spatial network relations are proposed respectively. The dynamic logical hierarchical relationship and dynamic migration relationship of the strict spatial network relation are given. Furthermore, this paper lay emphasis on the study of the dynamic conversion and adjacent relevance of the extended complex spatial network, and some instance analyses are provided. The research achievements in this paper is rather suitable for dealing with issues about the representation, error correction and forecast of the dynamic complex spatial network relations, therefore, the ability of spatial database to deal with spatial relations of complex spatial object has been enhanced.

In the feasibility analysis of R&D program, the data used to analyze the impact/trends/level of technology derive mostly from patents and theses. However, there is limitation in reflecting the newest technology trends data based on patents and theses. That is because of the occurrence of a one or two year gap time before these patents(or theses)are actually published or granted. Therefore, not only are related patents and theses data collected but, the extensive trends data from public web sites and social networks also need to be collected and analyzed. It takes a great deal of time, and manpower for these related feasibility analysis to happen successfully. To solve this issue, this analysis presents a methodology not only to rapidly and accurately collect data but, to efficiently analyze the newest technology trend flows. To analyze technology impact, phases of the data extraction, the application of measuring model and the determination of TIIB (Technology Impact Index based on Big Data) are processed. This theses proposes that the data analysis methodology used to find out the latest technology trends could also be useful for optimizing efficiency when analyzing. Moreover, the newly developed TIIB enables us to check the interest trends of the technology by reading the yearly changes.

With the development of science and technology as well as the advancement of national strategic deployment, study of hybrid heterogeneous system based on big data query has become a hot topic in internet industry both at home and abroad. Information technology has been widely applied in various fields and information has experienced explosive growth. However, different storage environments, collection systems and implementation platforms of information have hindered the communication and sharing of data between platforms and contributed a lot to deficient utilization of data. Thus the concept of heterogeneity comes into being. Heterogeneity in information system refers to difficulties in data utilization due to various data formats. This paper attempts to discuss the heterogeneous data integration methods based on big data and make an analysis. It explores advantages and convenience of design scheme on the basis of LDAP and offers detailed extracting rules for a better visual understanding of the corresponding model on its application.

Recently network data domain knowledge updates quickly, but with the growth of the large amount of information, the stability of the information itself decreases dramatically. So, one of the key research directions is that how to dig out the valuable information from the unstable and chaotic huge information. The research on rules getting incomplete information is helpful for getting more useful information. When the incomplete information turns into complete, it will cause a certain degree of information distortion. For this problem, the paper proposes the decomposition method of incomplete information system. This method, without completion process of incomplete information, selects a template through a template function. The template function is based on the rough set theory, and when ensuring the template, it can extract subset from incomplete information through decreasing step by step. Incomplete information system need to use an intermediate variable based on rough set theory when it is broken down by simplified rule sets.

Based on education, cognitive psychology and constructivism learning theory, this paper put forward multiple cooperative motion recognition strategy, and established the multi-sport cooperative learning model, namely, the cooperation from teachers to group experts, and then to multimedia, finally to learners. In order to verify the validity, we designed and implemented the questionnaire and teaching experiment, taking students from Inner Mongolia University of Science and Technology as the research object, and analyzed the experimental data by means of association rule mining technique and mathematical statistical analysis. Analysis results show that multiple collaborative learning methods can effectively enhance students learning interests of physical study and improve their physical skills, helping to cultivate the ability of cooperative learning among students. The association rules mining results show that teaching strategy has a very high correlation with the method, and using group cooperation synchronization classroom teaching can achieve the best teaching effect.

The objective of this study was to evaluate the effects of consumers’ Internet search behavior regarding online culture content on medical tourism. For this study, we categorized online culture content into dramatic (drama) and popular (pop) culture. We analyzed the influence of online culture content on medical tourism and the effect of innovation and imitation on the diffusion process. The following major findings and implications were identified: (1) an analysis of search behavior revealed that online culture content influenced medical tourism, (2) evaluations of online culture content differed according to type of content (i.e., pop culture was a more important influence than drama culture on consumers’ medical tourism intentions), (3) pop culture content supported the diffusion of medical tourism for a longer period of time compared with drama culture content, and (4) in contrast to the innovation coefficient, the imitation coefficient of pop culture was higher than that of drama culture. The findings of this study may provide a better understanding of the effect of consumers’ search behavior on the global diffusion of medical tourism. To advance the knowledge obtained in this study, future studies should focus on medical tourism products and marketing.