This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Advances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure 2010 work-programme, data and compute infrastructures and their underlying technologies, either oriented to tackle scientific challenges or complex problem solving in engineering, are expected to converge together into the so-called knowledge infrastructures, leading to a more effective research, education and innovation in the next decade and beyond. Grid technology is recognized as a fundamental component of e-Infrastructures. Nevertheless, this emerging paradigm highlights several topics, including data management, algorithm optimization, security, performance (speed, throughput, bandwidth, etc.), and scientific cooperation and collaboration issues that require further examination to fully exploit it and to better inform future research policies. The paper illustrates the results of six different surface and subsurface hydrology applications that have been deployed on the Grid. All the applications aim to answer to strong requirements from the Civil Society at large, relatively to natural and anthropogenic risks. Grid technology has been successfully tested to improve flood prediction, groundwater resources management and Black Sea hydrological survey, by providing large computing resources. It is also shown that Grid technology facilitates e-cooperation among partners by means of services for authentication and authorization, seamless access to distributed data sources, data protection and access right, and standardization. Ó 2011 Elsevier B.V. All rights reserved.
A significant number of graph database systems has emerged in the past few years. Most aim at the management of the property graph data structure: where graph elements can be assigned with properties. In this paper, we address the need to compare the performance of different graph databases, and discuss the challenges of developing fair benchmarking methodologies. We believe that, compared to other database systems, the ability to efficiently traverse over the graph topology is unique to graph databases. As such, we focus our attention on the benchmarking of traversal operations. We describe the design of the graph traversal benchmark and present its results. The benchmark provides the means to compare the performance of different data management systems and gives us insight into the abilities and limitations of modern graph databases.
Nowadays, in the era of every "things" connect together via the Internet, mobile device number has increased exponentially up to tens billions around the world. In line with this equipment increase, generated data amount is enormous and have attracted malefactors to spoil. For hackers, one of the popular ways to threaten mobile devices is to spread malware, which is very difficult to prevent because the application installation and configuration rights are set by owners, who usually have very low knowledge or do not care about security. In this study, we aim at improving security in environment of mobile devices by proposing a novel system to detect automatically malware intrusions. Our solution thus is built based on modeling user behaviors and using heuristic analysis approach to mobile logs generated during the device operation process. Although behaviors of individual user have a significant impact upon social cyber-security but achievement of user awareness still remains one of the major challenges today. For this task, a light-weight semantic formalization in form of physical and logical taxonomy is proposed for classifying the collected raw log data. Then we use a set of techniques like sliding windows, lemmatization, feature selection, term weighting and so on to process data. Meanwhile, malware detection tasks are performed based on incremental machine learning mechanisms because tasks' complex degree. Our solution is developed in the manner of allowing scalability in which the system is composed by several component blocks that cover preprocessing raw collected logs from mobile devices, automatically creating datasets for machine learning methods, using the best selected model for detection suspicious activity surrounding malware intrusions and supporting decision making using predictive risk. The proposals are experimented cautiously and gained test results show the effectiveness and feasibility of our malware detection system in applying to large-scale mobile environment.
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