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There are various shortages of the traditional data center in cost, resource utilization, power consumption and operation. This paper studies the development trend of the virtual data center and its technical advantages, proposes a scheme of virtual system of smart transportation data center based on VMware vSphere. At the same time, oriented on requirements of traffic data stream management in city smart transportation system under the environment of cloud computing, aimed at massive, multi-source, real-time, dynamic uncertain data stream sent back from all kinds of cross regional intensive control perception device, this paper analyses the characteristics and correlation of actual city traffic operation and traffic data flow, researches evolution mechanism of uncertain data; construct traffic data flow model based on ontology, core metadata and theory of constraints. And on this basis, this paper uses virtualization and large data sets of parallel processing, considers load balancing and adaptive mechanism, combines fuzzy theory and dynamic multi object, multi constrained decision theory, to seek the efficient query algorithm for dynamic, complex and continuous transportation data stream.

Based on efficient continuous parallel query series algorithm supporting multi-objective optimization, by using visual graphics technology for traffic data streams for efficient real-time graphical visualization, it improve human-computer interaction, to realize real-time and visual data analysis and to improve efficiency and accuracy of the analysis. This paper employs data mining processing and statistical analysis on real-time traffic data stream, based on the parameters standards of various data mining algorithms, and by using computer graphics and image processing technology, converts graphics or images and make them displayed on the screen according to the system requirements, in order to track, forecast and maintain the operating condition of all traffic service systems effectively.

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

The analyzing and processing of multisource real-time transportation data stream lay a foundation for the smart transportation's sensibility, interconnection, integration, and real-time decision making. Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handlingSkylinecontinuous query in the mass distributed uncertain transportation data stream. In this paper, we gave architecture of layered smart transportation about data processing, and we formalized the description about continuous query over smart transportation dataSkyline. Besides, we proposedmMR-SUDSalgorithm (Skylinequery algorithm of uncertain transportation stream data based onmicro-batchinMap Reduce) based on sliding window division and architecture.

Background X-linked inhibitor of apoptosis protein (XIAP) is a member of the inhibitor of apoptosis family of proteins and deregulation of XIAP can result in tumorigenicity. The objective of this study was to evaluate the prognostic significance of XIAP expression in colorectal cancer (CRC). Methods RT-PCR was performed to detect the expression of XIAP mRNA in CRC cells and tissues. The expression of XIAP protein in tissues was measured by immunohistochemistry. The correlation of XIAP expression with clinicopathologic factors and prognosis of CRC patients was evaluated. Results CRC cells showed significantly higher levels of XIAP mRNA expression than normal human intestinal epithelial cell. The expression level of XIAP mRNA in CRC samples was significantly higher than that in corresponding non-tumor samples. XIAP staining was positive in the cytoplasm of CRC cells. Higher XIAP protein expression was significantly correlated with tumor differentiation (P = 0.016), venous invasion (P = 0.039), and Duke's staging (P = 0.002). Moreover, XIAP-high group showed lower disease-free (P = 0.0136) and overall survival (P = 0.0084) rates than XIAP-low group. Multivariate analysis indicated that the status of XIAP expression was an independent prognostic factor for CRC (P = 0.0206; HR: 2.730; 95% CI: 1.226–5.445). Conclusion The status of XIAP expression might become an independent prognostic marker for CRC. J. Surg. Oncol. 2009;100:708–712. © 2009 Wiley-Liss, Inc.