On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of workflow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of "necessity and sufficiency" are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

With the rapid development of high-speed railway, the equipment life-cycle management data are generated in large scales which run through the period of production, operation, maintenance, falling into a notion of Big Data. There is broad recognition of value of data and information obtained through analyzing it. The exponential growth in the amount of railway-related data means that revolutionary measures are needed for data management, analysis and accessibility. At present, the promise of data-driven decision-making is now being recognized broadly. How to store the big data efficiently, reliably and cheaply are important research topics. This paper proposes a framework of data management of high-speed railway equipment, where cloud computing provides a feasible technical solution combined with MapReduce programming model based on Hadoop platform. These models are capable of considering the characteristics of data and processing demand in management of High-speed railway equipment. Finally, we summarize the challenges and opportunities with Big Data for application of China railway and point out there is more than enough that we can work on.

Safe operation is the basic requirement of railway operation. Reasonable maintenance is the basic guarantee for the safe operation. Big data to analyze things as a rule of a cutting edge technology, applied to the analysis of railway detecting data processing, can improve the testing data of the use of the quality and efficiency of analysis. Based on big data, this paper points out state railway equipment management, reasonable status classification, solving the state transition probability, the prediction model is set up, providing better decision support for maintenance and repair work.

Maintenance of railway permanent way infrastructure plays a vital role in protecting the safety of railway transport. The infrastructure management of railway permanent way rarely involves effective information technology, causing the lack of supplies basis and the difficulty in tracking the relevant information of materials when problems arise in the facilities performance. This article introduced the concept of life-cycle management into the management of maintenance-of-way infrastructure and studied the information coding work on various stages of the life cycle. Simultaneously, the paper discussed how to integrate the resource of facilities and materials with the information management of maintenance-of-way infrastructure in the light of this theory.