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The mobile Internet makes people can access the Internet whenever and wherever possible, in the library and online browsing, due to lack of communication between the various interlibrary resources, which cannot be fully utilized, in order to solve this problem, this article constructed the online library management system based on cloud platform, this system can fully scheduling online book information, and realize the analysis of library needs and the needs of the population, and it has the function which is book recommendation.

The capability of distributed computing for overriding the requirements for sending different frameworks for running a server based administrations raised a progressive change in the way the conventional requests of the individuals utilization to be taken care of. Distributed computing gives the rental support of the client in which a client can utilize the specific programming by paying for that on the cloud server. Since the entire situation is useful for enormous businesses like facebook, google, orkut and so on, different fields are likewise getting reliant on distributed computing. Since huge amounts of information is transferring consistently to the cloud server does should be dug appropriately for productive information stockpiling. In this paper, we attempt to coordinate the information preprocessing strategy with an information grouping method to mine enormous information's of asthma based patients. We have utilized simulation tool called eclipse to run the Programming interface's of weak and cloudsim for setting up the trial environment.

With the rapid development of computer science and technology, data mining modelling techniques have emerged and rapidly developed as an alternative powerful meta-learning tool to accurately and fast analyze the massive volume of data generated by modern applications. The combination of data analysis technique and evaluation of public servant execution is urgently needed. Improve the execution of public servants at the grass-roots level is one of the important link to strengthen the construction of authority administrative efficiency of administrative goals is very important. Enhance the execution must first cultivate advanced concept, armed with advanced execution concept to the vast number of public servants at the grass-roots level. The assessment of public execution has a lot of traditional methods and models can be used but there is limitation. The limitation could be concluded as the following. Carelessness or poor sensitivity, At the grassroots level, the implementation of the main body of the general public servants at the grass-roots level and they can perform in place, one of the important factor is whether the leader on the work division of labor, organization, management and supervision effectively. In this paper, we conduct research on evaluation of public servant execution based on data mining technique and joint modeling analysis of multiple factors under big data environment. Firstly, we introduce some state-of-the-art clustering algorithm to serve as the basis of our model. Combined with deep neural network and optimization modelling, we propose our support vector machine based data clustering algorithm through multiple factor modelling. Subsequently, we discuss the principles on evaluation of public servant execution and process management. In the experimental part, we conduct experiment on both data clustering based data pre-processing step and the evaluation of elements’ weight for process management. The result indicates the most important factor for management and the feasibility and effectiveness of our proposed clustering method. Future potential research areas are also discussed in the final Section.

Every day, there are a large number of new questions produce in question answering community, how to find the answer user for the question and sort candidate answers is the research content of this paper. First of all ,we use statistical language model to model for user interest, make full use of the abundant personalized information in question answering community to find out user interest distribution, and obtain the user list of question recommendation by introducing the query likelihood language model to calculate the degree of user interest to the new question. Secondly, we calculate the matching degree of question and candidate answers through fusing the feature of word form, word order, distance and semantic. The candidate answers of question will be sorted automatically, making it easier for users to choose the best answer. Experiments are performed on data sets extracted from the Baidu know, experimental results show that the method proposed in this paper has better performance.

As a result of tremendous rise in internet usage like social media and forums, mail systems, scholarly and research articles, daily online transactions from multiple sources like health care systems, meteorological and environmental organizations etc., the data collected has shoot up exponentially. This vast collection of data, called Big Data, has caused the traditional tools incompetent for managing it from either of storage, computing or analytical perspective. There is an immense need of architectures, platforms, tools, techniques and algorithms to handle Big Data. The available technologies deal with two broad aspects related to Big Data that are Big Data Storage Management and Big Data Computing, focused to overcome various challenges such as scalability, faster processing speed, multiple format data processing, availability, faster response time and analytics etc. This paper reviews recent trends of storage and computing tools with their relative capabilities, limitations and environment they are suitable to work with.

To effectively predict auto sales and improve the competitiveness of automotive enterprise, the characteristics of actual auto sales were analyzed, owing to the seasonal fluctuations and the nonlinearity of monthly sales, the combination forecasting model based on seasonal Index and RBF neural network was proposed. The weights of the two single models were computed using mean absolute percentage error and the sum of square error respectively, the result shows that mean absolute percentage error is more effective. Finally, the prediction accuracy of different models was compared based on the criteria of MAPE and RMSE, and the effectiveness of the method was proved, the proposed model can take advantage of the strengths of the two single models, the results indicate that the combination forecasting model suitable for auto sales has high prediction accuracy, which can provide a certain reference to auto sales forecasting.

Twitter is an online social networking site which contains rich amount of data that can be a structured, semi-structured and un-structured data. In this work, a method which performs classification of tweet sentiment in Twitter is discussed. To improve its scalability and efficiency, it is proposed to implement the work on Hadoop Ecosystem, a widely-adopted distributed processing platform using the Map Reduce parallel processing paradigm. Finally, extensive experiments will be conducted on real-world data sets, with an expectation to achieve comparable or greater accuracy than the proposed techniques in literature.

In this paper, features of high-dimensional data are analyzed, and existing problems of the Canonical Correlation Analysis (CCA) are analyzed for a single view of a full supervised view data. In order to improve CCA, we introduce the method of classifier and present a Classifying to Reduce Correlation Dimensionality (CRCD). Meanwhile, combining big interval learning method, we propose the big correlation analysis (BCA). At last, experiments are respectively conducted by using artificial data set and UCI standard data set. The result shows that methods are feasible and effective.

According to the theory proposed by former researcher, this paper puts forward the assumptions of public cultural services upgrading and builds a conceptual model. Data collected with the method of survey. The proposed path hypotheses are tested by structural equation modeling. And ultimately, the path relationship of enhancing the level of public cultural services is obtained

Wireless sensor network consists of a huge quantity of less-price sensor nodes. These nodes has restricted power of battery, and the replacement of battery is not a simple task in wireless sensor networks because there are a huge quantity of nodes. Data Aggregation is a significant method to attain power efficiency in wireless sensor network. Data aggregation at the sink by all the nodes results in flooding of the data which causes greatest energy utilization. Though a lot of protocols are planned so far to get better the energy efficiency further but still a lot improvement can be made. In this paper, various data aggregation techniques have been discussed. The overall purpose of this survey is to explain data aggregation techniques and to find limitations of General Self-Organized Tree-Based Energy-Balance Routing Protocol (GSTEB).

This study identifies the job quality among the working poor in Harbin from five aspects: physical working environment, psychological working environment, job security and flexibility, job satisfaction and income. Three hundred and seventy-seven working poor people are recruited using convenience sampling strategies. Factor analysis and cluster analysis results indicate that there are three types of job quality groups among the respondents. Low job quality group shows the characteristics of adverse working environment, informal employment, middle ranking on relationship with co-workers, and low satisfaction on job quality. On the contrary, high job quality group presented the characteristics of comfortable working environment, formal employment, high ranking on relationship with co-workers, and high satisfaction. Multiple regression results indicate that gender, hukou system and educational background are closely associated with job quality. Female’s job quality is higher than male’s. People with nonagricultural hukou keep a higher job quality than people with agricultural ones. People who graduate from university keep a higher job quality than others.

With the development of network economy and information technology, the importance of logistics network has been gradually recognized by enterprises. In this paper, we make an empirical analysis of enterprise logistics network performance, and build the evaluation index by using four dimensions as inventory, transportation, information level, comprehensive competitiveness. Result shows that this evaluation model has strong practical significance, and can help e-commerce enterprises to improve their logistics network performance. Therefore, enterprise should start from two angles as efficiency and effect, efficiency evaluation is mainly aimed at minimize the resource cost. At the same time, the main purpose of the evaluation is to ensure that the logistics network can achieve the goal of the enterprise, and provide more effective service.

Named entity (NE) translation is a fundamental task in machine translation (MT) and cross-language information retrieval (CLIR). Furthermore, Organization name (ON) translation is the most complex among all the NEs. A novel system for translating ONs from Chinese to English, with a translation model and web resources, is proposed. Firstly, we built a translation model with Chunk. Then query expansion was adopted with the translation model and term-subject co-occurrence. Thirdly, we extracted the Chinese Organization names with English sentences using the method of frequency shifting and adjacency information to find English fragments as translation candidates. Finally, we found the best translation by computing the trustworthiness of all candidates. The experimental results showed that the approach returned a better performance than machine translation-based systems.

Fast distribution of digital contents through open networks is not posing a significant problem due to digital media revolution. Modern technologies have also reduced the reproduction time of digital media and its fast distribution. However, this facility has also the darker side where unauthorized users can tamper its contents and manipulate the digital data thus giving rise to serious security concerns. This problem has to be addressed very seriously. Digital watermarking techniques have recently evolved to address the above problems. The usage of these digital watermarks prevent illegal reproduction and usage of digital data as well as help in identifying the origin, author, owner etc even after various manipulations or attack on the digital data. A number of watermarking techniques in spatial and frequency domain were given by various researchers which suffered from problems robustness. Genetic algorithm provides an alternative way of creating watermarks with Promising values of robustness aspect of watermarking. This paper deals with design and development of a new watermarking technique which uses genetic algorithm to identify locations within the cover image for watermark insertion in spatial domain and then apply the average neighborhood concept for the purpose of watermark insertion and extraction ensuring higher robustness and resilience to several possible image attacks. Genetic search often produces same watermark locations in different populations for watermark insertion resulting in poor value of robustness, which need to be checked. Sliding window concept introduced in this paper uses a set of a few genes which are serially shuffled to get new set of locations for watermarking during each population generation and helps in enhancing robustness aspect of watermarking. Roulette-wheel selection has been used while using the genetic algorithms developed in the paper.

The data distribution in the data streams usually changes dynamically with time. Traditional mining algorithms based on transaction are difficult to establish the correlation between time characteristics and relationship features, thus making the results inaccurate. By analyzing the problems in the processing of time data stream, we put forward the concept of time gap degrees and design a mining algorithms based on weighted FP-Tree. We introduce the concept of FP-Tree node weights to transform the time data dynamically and excavate the data stream association rules. The experiments performed on the actual data set show that the algorithm can improve the recall and precision while consumes comparable computational time.

Many text classifications depend on statistical term measures or synsets to implement document representation. Such document representations ignore the lexical semantic contents or relations of terms, leading to losing the distilled mutual information. This work proposed a synthetic document representation method, WordNet-based hybrid VSM, to solve the problem. This method constructed a data structure of semantic-element information to characterize lexical semantic contents, and support disambiguation of word stems. As a template, lexical semantic vector consisting of lexical semantic contents was built in the lexical semantic space of corpus, and lexical semantic relations are marked on the vector. Then, it connects with special term vector to form the eigenvector in hybrid VSM. Applying algorithm NWKNN, on text corpus Reuter-21578 and its adjusted version, the experiments show that the eigenvector performs F1 measure better than document representations based on TF-IDF.

With the development and popularization of the Internet, the different microblog topics are disscussed everyday, so the microblog can product a large number of various topics,which can reflect the influence of different users in a given topic. In the microblog topics,the key users of microblog topics are found by discovering the influential sensitive topics and calculating the influence value of the user,which are the focus of attention in the fields of microblog public opinion supervision and safety management. In order to accurately measure the influencers in the given topic and to calculate the user influence value, the thesis proposes the method of constructing the propagation network which is based on attention and forward relationship between users, and then proposes TDN-If algorithm by using PageRank algorithm. When we calculate the transition probability in propagation network by using the TDN-If algorithm, the information propagation is considered to measure influencers. This method can resolve the defects of discovering the user's influence by only using followers of this single indicator in the current microblog topics. The experimental results show that the TDN-If algorithm has important theoretical and practical value, which is better than TwitterRank algorithm and other influential individuals found algorithm. Thus, the method proposed in this paper can not only effectively solve the problem about discovering and persuading the key users in the sensitive topics who have influences and have the unique insights on the significant events, for example, which can provide the strong guarantee for the governments in the fight against terrorism, but also provide the important theory and method for the complex network community discovery, microblog public opinion supervision , microblog safety management and so on.

Researches concerning to the temporal expressions in minority languages, particularly the Uyghur temporal word recognition, has not previously been conducted. this paper stated for the first time the relevant progresses and significances on domestic and overseas researches. We analyzed the formation of simple and compound temporal words in modern Uyghur language. We discussed the constitutive rule for Uyghur temporal expressions and put forward a new rule for combined temporal expression templates on the basis of dictionary and regular expressions. We then designed a suitable recognition algorithm and implemented an extraction system for the Uyghur temporal expressions. In the end, the feasibility and usability of the complete recognition method and results were discussed.

Because the properties of data are becoming more and more complex, the traditional data classification is difficult to realize the data classification according to the complexity characteristic of the data. Support vector machine is a machine learning method with the good generalization ability and prediction accuracy. So an improved ant colony optimization(ACO) algorithm is introduced into the support vector machine(SVM) model in order to propose a new data classification(ERURACO-SVM) method. In the ERURACO-SVM method, the pheromone evaporation rate strategy and pheromone updating rule are introduced into the ACO algorithm to improve the optimization performance of the ACO algorithm, and then the parallelism, global optimization ability, positive feedback mechanism and strong robustness of the improved ACO algorithm is used to find the optimal combination of parameters of the SVM model in order to improve the learning performance and generalization ability of the SVM model and establish the optimal data classification model. Finally, the experimental data from the UCI machine learning database are selected to validate the classification correctness of the ERURACO-SVM method. The experiment results show that the improved ACO(ERURACO) algorithm has better optimization performance for parameters selection of the SVM model and the ERURACO-SVM method has higher classification accuracy and better generalization ability.

Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Improved Quantum Genetic Optimization with FCM algorithm is proposed. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum hadamard gate. The gradients of object function are utilized in adjusting the value of rotating angle by a dynamic strategy. Each chain of genes represents a optimization result, Therefore, a double searching space is acquired for the same number of chromosomes. Experimental results show that the proposed method improves the stability and the accuracy of classification．