Applied Mechanics and Materials Vols. 263-266

Abstract: Due to the huge scale and the number of components, big data is difficult to work with the use of relational databases and desktop statistics and visualization package. Much database replication technology is used to increase the MTTF, but few have a large database system, the traditional method of backup is not feasible, expensive manpower costs reduce MTTR. On the basis of analyzing the characteristics of data in large databases, we propose a new method called Detaching Read-Only (DRO) mechanism and its changes DRO+. It reduces MTTR by reducing the physical change of the data in each database, by separating data node size granularity. Analysis and experimental results show that our method can not only reduce the MTTR an order of magnitude, but there is no additional hardware costs, but also reduce the high human costs.

Abstract: Existing research focuses on document-based sentiment analysis and documents are represented by the bag-of-words model. However, due to the loss of contextual information, this representation fails to capture the associative information between an opinion and its corresponding target. Additionally, several researchers focus on sentence-based approaches, which can effectively extract an aspect-sentiment word pair within one sentence. Nevertheless, their approaches can only deal with one aspect within one sentence and miss the identification of sentiment modifier. In order to solve these problems, this paper proposes a novel identification approach of aspect-modifier-sentiment word triple using shallow semantic information. Experimental results show that our approach is feasible and effective.

Abstract: In this paper, we focus on the allocation model of the marketing resource in CRM. We developed a bi-level programming model based on the customer equity theory. This model will help the corporate to coordinate the marketing resources allocation process with customer equity growth. It will strengthen the capacity of customer relationship management and keep the competitive advantage in a long run.

Abstract: BP neural network is a widely used neural network, with advantages as adaptability, fault tolerance and self-organization. However, BP neural network is difficult to determine the network structure, and easy to fall into local minimum points. In this paper, an optimized BP neural network was proposed based on DS, he advantages of DS Evidential Reasoning on uncertain information are used to improve the recognition rate and credibility of BP. Experiments on Heart Disease Data set shows the proposed method have good performance on run time, prediction accuracy and robustness.

Abstract: Although the virtual desktop reduces the complex management and maintenance of the traditional desktop environment, it will increase the maintenance complexity of the user operating environment and reduce the efficient utilization of local resources. To address the above issues, a novel diskless virutual desktop framework is proposed in this paper. In this framework, the operating environment is centrally managed, which is network booted from the image server. Then by means of the remote virtual machines, the capability of the runtime is extended by both the local and remote resources. Furthmore, a web-based browser shell is provided where both the local and remote resources are transparently provided to schedule the tasks among them. An experiment is carried out which verifies the feasibility of the framework.

Abstract: Based on product design process knowledge reuse, according to the characteristics of ontology knowledge representation, process ontology knowledge representation method is offered. The paper extracts the category of design process, defines and describes the attribute and relationship of these categories. Then, the design process ontology database based on process ontology is constructed. As a result, it supports to achieve the sharing and reusing of design process knowledge.

Abstract: It is significant in theory and practice to develop and exploit online supervision system of sports games,which is helpful to improve sports games’efficiency and social economic benefit. The outcome of this study is as follows:the sports games online supervision system based on the wireless network has the advantages of convenience, mobility,combination, economy, practicality and high efficiency. It is easy to achieve the standardization of data structure and work flow. Using the online equipment of 802.11b allows a further development of the function of real_time image.

Abstract: Temporal and Spatial database of moving objects on fixed network involves frequent updates. Information on query about the historical, present and future positions of moving objects is still limited. The current study propose a new index model (RRH) based on R*- tree, R-tree and Hash table. The R*-Tree is used to index the spatial data of the network. The R Tree forest is employed to index the time intervals. A composite hash table and link list storage structure is used to queries retrieve based on trajectory. The exponential smoothing algorithm is applied to forecast position/speed of the moving objects for a given time. The analysis results show that the proposed model has higher efficiency position prediction.

Abstract: The RD performance of distributed video coding (DVC) is lower than conventional video coding. This article analyzes the reason of that, finds during the decoding process DVC does not make full use of the set of side information in the distributed framework. Then paper proposes a way to improve the prediction accuracy, during the process of generating predictive side information, makes full use of the distributed structural characteristics, improves the RD performance of DVC by using bidirectional motion estimation and the weighted average methods

Abstract: We proposed the improved wavelet neural network and applied it to forecast rainfall. Activation function was selected automatically from three wavelet basis functions. The three given wavelet basis functions were Morlet, Mexican-Hat and Different Of Gauss(DOG) wavelet basis functions respectively. We demonstrat its effectiveness by forecasts rainfall of some cities in China. The results show that the improved wavelet networks method is better than BP neural network method in rainfall forecast.