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The factor of input in forestry is a key reason for the economic growth of forestry. This paper divided the country's 31 provinces (autonomous regions) into three categories by system clustering method, and constructed Panel Data Models respectively, in order to analyze the contributions of fixed forestry assets investment and labor input to forestry economic growth. The results show that three types of regions in the provinces of forestry factor inputs are quite different in promoting the forestry economic growth: labor inputs is the main driver of the economic growth, the forestry investment as a leading role in forestry is relatively weak for the forestry economic growth, namely the forestry economic development in China is still a labor-intensive type.

This research work investigates the correlation of software function point elements using bootstrap simulation. The correlation of software function point elements plays an important role in understanding the software size; the correlation among function point elements suggests that they measure the same attribute of a software project. Bootstrapping is an effective method to study the statistical properties of correlation coefficients; bootstrap produces a histogram of the possible values of correlation coefficients, which helps to understand the range and spread of the correlation among different function point elements, rater then generating a single point estimate of the correlation.

The paper studied hypoid gear CNC machining theory and simulation processing. Through construct a simulation case of hypoid gear machining system, optimization processing parameters for the main aspects, verify the effectiveness of pre-correction system.

Service Discovery is done based on Keyword Match, Simple Semantic Description Services, and Rich Semantic Description Service. Web Services raise the web to a new level by integrating computational aspects. Web Services are accessed by computer programs but these still need help by humans. Web-services should be described in a formal, semantic way, so programs can find, compose and invoke them automatically. Existing system discover the service based on QoS (Quality of Service). It uses the Agent to discover the service, but normalization & classification are not done by Agent here. Existing system are using the selection algorithm, which works in association with QoS value, based on consumer requirements. So In our proposed system we proposed the Normalized, Classification, & Ranking of the QoS parameter based on minimization maximization criteria using agent based architecture. We rank the services according to their QoS levels and their services matching.

With Fujian Province as object of study, panel data from 1981 to 2012 is selected in this paper. Besides, VAR model is established. Co-integration test, Granger causality, impulse response function and variance decomposition are used for quantitative analysis on correlation between domestic investment, government consumption, household consumption and economic growth in Fujian Province. The author reveals long-term stable balance among investment, government consumption and household consumption in Fujian Province as well as bidirectional causal relationship between economic growth and domestic investment, government consumption and household consumption. For a short term, household consumption is of relatively strong positive impact effect on economic gain, which is served as major impetus of economic growth. Domestic investment is of relatively obvious promotion for economic growth in a short term and of certain negative effect on economic growth in a long term. However, government investment is of relatively small positive impact to economic growth. Such promotion lasts for a longer period. On this basis, the author proposes some policies and recommendations for macroeconomic regulation and control in Fujian Province.

Agriculture is the basic industry of our country, the development of agricultural economy in relation to the overall goal of modernization in china. With the contribution of science and technology to economic growth gradually strengthen, countries will also application of science and technology to improve the competitiveness of the agricultural economy. In this paper, we analyze the influence of R&D investment on agricultural economy based on panel data, the result shows that: there exist a long-term stable equilibrium relationship between R&D investment and agricultural economy, the influence coefficient of R&D investment to agricultural GDP is 0.19. Meanwhile, agricultural researchers also have 0.10 elastic contributions to agricultural economic growth, so that agricultural talent is also an important factor of agricultural economy. Therefore, the government should strengthen investment in agricultural, improve the technological innovation ability and establish long-term mechanism of continuous fund supply for agricultural science and technology innovation.

In data mining one of the challenging problems is how to handle high dimensional and complex datasets. Decision trees when applied to high dimensional and complex datasets produce decision trees which are very complex in nature and thereby reducing generalization. To address this issue we propose an algorithm know as Radom Matrix Projection with Outlier Detection (RMPOD). The proposed algorithm is validated on 24 UCI datasets against accuracy and tree size metrics. The results of the proposed algorithm with compared algorithm suggest an improvement in accuracy and tree size for better generalization.

Price wars are a major form of competition for Chinese online retailers. Based on empirical data from online retailers, JD Mall, Amazon(Z.cn) and Dangdang, this study explored the price competition in China's B2C e-commerce market. The average price level, the minimum price level, the price differential level and price variation were considered. The results showed that the average price levels between the three e-commerce websites had statistically significant differences. However, the minimum price level and the price differential level were similar. In terms of the price variation, the three websites adopted different price adjustments and did their best to avoid a direct price war. This suggests that the e-commerce market competition in China is becoming rational.

Multi-tenant data storage model has multiple solutions and comparing the different storage solutions can help users improve their work efficiency. This paper proposes a query performance evaluation method based on the relational algebra. First of all, we introduce three wide table models. Secondly, we unite the format of tenant query SQL statement by analyzing structure of storage model, replace the unified format SQL with the relational algebra and evaluate the I/O cost of SQL query using relational algebra. Finally, through theoretical calculations and experimental simulations, we evaluate the performance of multi-tenant storage model according to query performance. The results show our evaluation method based on relational algebra provides new perspective for the study of performance evaluation in multi-tenant data model.

In this paper, on the basis of traditional phonetics, by using the methods of experimental phonetics and voice pattern theory, Analyzed and summarized the vowel pattern of the monosyllables in Uyghur language. The statistical analysis is carried upon the vowel formant frequency values in monosyllables, and discussed by using Joos method in more details. For the first time, with the actual experimental data proves the accordance of tongue location features of Uyghur vowel with the traditional knowledge from hearsay. The research results of this paper will have a high reference value for the study and application development of both Uyghur language and the other languages are belongs Altaic language family.

Cassandra database system is one of the universal databases. To achieve high performance, we should allocate memory space rationally according to actual demands. Otherwise, it will influence reading and writing performance. Actually, we always allocate memory space according to experience and repeated attempts which usually won’t give us the best answer. To solve this problem, firstly we analyze the reading and writing processing of the Cassandra database and find out the corresponding memory space which will influence system performance. Secondly, we build up a relationship model between system performance and memory allocation and name it as The Memory Model of Reading and Writing Performance. We have already applied the relationship model to real database servers to guide memory allocation and performance evaluation. Simulation results show that this memory model could well describe the quantization relationship of memory space and system performance.

The data cleaning of electrical power big data can improve the correctness, the completeness, the consistency and the reliability of the data. Aiming at the difficulties of the extracting of the unified anomaly detection pattern and the low accuracy and continuity of the anomaly data correction in the process of the electrical power big data cleaning, the data cleaning model of the electrical power big data based on Spark is proposed. Firstly, the normal clusters and the corresponding boundary samples are obtained by the improved CURE clustering algorithm. Then, the anomaly data identification algorithm based on boundary samples is designed. Finally, the anomaly data modification is realized by using exponential weighting moving mean value. The high efficiency and accuracy is proved by the experiment of the data cleaning of the wind power generation monitoring data from the wind power station.

Due to the mass track data accumulated day by day, new challenges are raised for traditional information retrieval. This paper studies the issue of k-nearest neighbor track retrieval facing moving object, and converts this issue into aggregate Top-k query issue of information retrieval field. A parallel TA algorithm in random access database is proposed, and it has effectively solved the issue of k-nearest neighbor track retrieval. Performance of this algorithm is verified through a large number of experiments.

In food consumption, food addiction is behavioral and biological overlaps have been observed between eating and addictive disorders. Food addiction influence to healthy life. Food addiction caused by overeating, bingo eating, eating disorder, eating addiction, mindless eating, craving, chocaholic, and emotional eating. Determination between addiction and normal condition in food consumption, need to classification. Classification is very important in determine signs of food addiction. Classification using Naïve Bayes Algorithm and Decision Tree Algorithm. Class target is Class Normal and Class Addiction. Classification using Naïve Bayes Algorithm by criterion is Calorie Dense Food, Fatty Food, Sweet Food, Diet and Stress. This criterion as causal factor of food addiction. Classification using Decision Tree Algorithm by criterion is Stress, Fatty Food and Calorie Dense Food. This criterion as causal factor of food addiction. The experimental result is a Classification Model. This model became data source for national policy in public health.

In order to deal with multiple user preferences and improve query efficiency, selection strategy is adopted for top-k query to depress the compare operations. Firstly, the kth order statistics are selected randomly along with partitioning the data set basing on it, and the top-k result set can be received after several recursive partitions. Secondly, to select the kth order statistics accurately, the approximate kth order statistics is choose as threshold according to the similarity of user preference and system preference, and the top-k query result set can be accessed through simple comparison. Finally, the time complexities of presented algorithms are analyzed and their correctness and completeness are proved respectively. The experimental results show that our algorithms improve the efficiency of top-k query greatly.

Measuring the similarity between nominal variables is an important problem in data mining. It's the base to measure the similarity of data objects which contain nominal variables. There are two kinds of traditional methods for this task, the first one simply distinguish variables by same or not same while the second one measures the similarity based on co-occurrence with variables of other attributes. Though they perform well in some conditions, but are still not enough in accuracy. This paper proposes an algorithm to measure the similarity between nominal variables of the same attribute based on the fact that the similarity between nominal variables depends on the relationship between subsets which hold them in the same dataset. This algorithm use the difference of the distribution which is quantified by f-divergence to form feature vector of nominal variables. The theoretical analysis helps to choose the best metric from four most common used forms of f-divergence. Time complexity of the method is linear with the size of dataset and it makes this method suitable for processing the large-scale data. The experiments which use the derived similarity metrics with K-modes on extensive UCI datasets demonstrate the effectiveness of our proposed method.

A new model retrieval method based on face matching sequence is proposed in this paper. Attribute adjacent graph is used to describe two faces’ geometry similarity and topological relationship in CAD model. According to the difference of edge numbers, similarities between two models’ faces are computed and face similarity matrix is constructed. Ant colony algorithm (ACA) is applied to obtain an optimal sequence of matching faces between two models. Accumulate similarity values of optimal matching faces to calculate two models’ similarity. Experimental results show that this method can evaluate two CAD models’ shape difference effectively.

The high dimensionality of feature space is a big hurdle in applying many sophisticated methods to text categorization. The feature selection method is one of methods which reduce the high dimensionality of feature space. In this paper, we proposed a new feature selection algorithm based on gravitation, named GFS, which regards a feature occurring in one category as an object, and all objects corresponding to a feature occurring in various categories can constitute a gravitational field, then the gravitation of a feature with unknown category label on which all objects in the gravitational field act is used for feature selection. We have evaluated GFS on three benchmark datasets (20-Newgroups, Reuters-21578 and WebKB), using two classification algorithms, Naïve Bayes (NB) and Support Vector Machines (SVM), and compared it with four well-known feature selection algorithms (information gain, document frequency, orthogonal centroid feature selection and Poisson distribution). The experiments show that GFS performs significantly better than other feature selection algorithms in terms of micro F1, macro F1 and accuracy.

There is ineffective classification problem in application of K-means clustering algorithm in massive data cluster analysis. This paper presents a K-means algorithm based on generalization threshold rough set optimization weight. Firstly, utilize attribute order described method, using the average distance calculation with Laplace method to optimize the generalization threshold of fuzzy rough set , then the Euclidean distance metric is used in the calculation of the similarity of K-means algorithm, introducing the variation coefficient into the cluster analysis, clustering the Euclidean distance weighted K-means algorithm totally based on data, finally, combine the rough set algorithm based on the generalization threshold optimization and K-means clustering algorithm, applied to medical and health data classification. The K-means algorithm based on generalization threshold rough set optimization weight presented by this paper has a better effect on medical and health data classification.

In order to solve the existing problems of case-based teaching, the construction of the independent case-based teaching system is proposed, and the connotation and advantage of independent teaching system are analyzed. With the construction of network teaching platform, case-based teaching process is optimized and function is reorganized through the system layering design and system function module design. The network platform provides support for the construction of the independent case-based teaching system. The independent case-based teaching can promote students’ active learnin