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With the fast development of network technology, great amount of data have been accumulated. Plenty of them are organized by using heterogeneous information networks (HIN). So mining heterogeneous information networks efficiently is very important. Node clustering is an essential part of this task. And several clustering algorithms have been proposed. As all these algorithms contain complicated optimization procedure and matrix calculation procedure, complexity of these algorithms is very high. To overcome the shortage described above, in this paper, a new clustering algorithm is proposed. In this algorithm, several parameters should be inputted. These parameters include a heterogeneous information network, meta-paths that are used and the names of target types. During the clustering procedure, a homogeneous network will be built by the proposed algorithm firstly. All the target objects of HIN are treated as nodes of this network. The instances of meta-paths are edges. After the homogeneous network is constructed, label propagation procedure can be performed. Then the clustering result will be obtained. Obviously, by using the proposed algorithm to perform clustering, the complex optimization procedure and matrix calculation procedure are eliminated. As the convergence rate of label propagation procedure is fast, the proposed algorithm is very efficient. Besides, we can find that label propagation procedure can be executed in parallel. Thus, the proposed algorithm is easy to be parallelized. In this situation, it is fit for processing large scale HIN based on server cluster. From experimental results, we can find that the proposed algorithm running faster than all the other algorithms for comparison.

Accurate prediction of high-speed traffic flow is of great significance for alleviating traffic pressure, reducing congestion, and reducing environmental pollution. Aiming at the low efficiency of the KNN prediction algorithm in the training and testing of traffic big data on a single machine, this paper proposes a distance-weighted KNN algorithm based on Spark. At the same time, cross-validation is used to tune the model K value, and the feature vector of the data is selected according to the inherent spatio-temporal correlation of the data. Experiments have proved that compared with traditional KNN, the weighted KNN model based on spatio-temporal feature relationships under Spark greatly reduces the time to find K nearest neighbors in the KNN algorithm, and the prediction efficiency is significantly improved while ensuring the prediction accuracy. This method can provide reliable support for highway management in the future.

All-to-All Comparison (ATAC) is a key computing mode to improve computing performance and is applied in many disciplines. Through the research on the data distribution strategy of the ATAC calculation, a novel solution framework of the ATAC data distribution strategy based on tabu search is proposed for the first time. An ATAC data distribution model is constructed, and the related algorithms of the model are designed based on the tabu search, and it is implemented on MATLAB. The framework consists of three parts: 1) the driver module responsible for interacting with users; 2) the load balancing module that solves the data distribution strategy that meets the load balancing situation; 3) the storage optimization module that solves the data distribution strategy that meets the load balancing and optimized storage. Experiments prove that the framework for solving the ATAC data distribution strategy based on tabu search has the following advantages: A) The data distribution strategy obtained by the framework enables the nodes of the distributed system participating in the ATAC calculation to achieve load balancing and complete data localization. B) The data distribution scheme can reduce 40%-50% of the storage space in the distributed system. C) The framework is more advanced than the existing ATAC data distribution algorithm in calculation speed.

Cloud computing has become more and more vital in digital world as a basic technique of many services. As the foundation of cloud computing, the cloud platform hosts various cloud services running on it and providing interfaces to cloud users. Thus, it becomes a high-value target in attacker's eyes. This paper proposes a novel attack pattern named DECH for intruding and controlling a cloud platform standing on the side of attackers. From the defenders' perspective, this paper points out the key measures to enforce cloud service security are to restrict the Trust Computing Base (TCB) size and strengthen the isolation between cloud components. Then we provide countermeasures against “Detect Escape Control and Harvest” (DECH) based on the combination of Software Guard Extensions (SGX) and Trust Domain Extensions (TDE). This research provisions references for building secure cloud services.

The problem of urban road congestion is the key to be solved urgently in China's urban traffic. To effectively predict it, this paper proposes a method for predicting urban road congestion based on the Spark platform parallel Gradient Boosting Decision Tree algorithm. First, the basic principle of GBDT algorithm is briefly introduced. Secondly, the GBDT algorithm based on the parallel design of the Spark big data platform is used to predict urban road congestion. Finally, through accuracy experiments and scalability experiments, the effectiveness of the algorithm and the performance of the algorithm under different numbers of nodes are verified in the Spark cluster. Experiments prove that the method proposed in this paper can effectively predict urban road congestion, reduce the running time, improve the prediction efficiency, and provide effective help for urban road management.

With the rapid development of information technology and internet finance, RFM model technology has been widely used in banking financial services. The traditional financial services industry has gradually changed from product center to customer center. As customer transaction data continues to grow in the database, it is urgent to analyze the transaction information with the high efficiency big data analysis technology. Therefore, an RFM model suitable for financial customers has been established, and this model has been applied to the financial product recommendation guidance system. It can greatly improve the quality of customer marketing services in the banking industry, and can effectively reduce operating costs.

This paper analyzes the Struts framework, the Hibernate framework, and the combined framework of Struts and Hibernate. The paper also discusses how to use the combined framework to realize the application and design of the management system for scientific research projects. This system can efficiently solve the problem of the poor expansibility and poor maintainability in the application of traditional Java Web.

Through intensive study of web page automatic generation technology, this paper proposes a web page code automatic generation system on the basis of XSLT technology and describes a practical implementation of the system. Web Page code automatic generate system can achieve auto-generation of underlying system and repetitive codes, reduce boring duplication of coding in software development, improve the efficiency of developer as well as enhance system maintainability.