报告包括电子商务方面的重要观点、政策与环境、网购平台、行业应用、电商物流、电商金融等研究。

报告包括电子商务方面的重要观点、政策与环境、网购平台、行业应用、电商物流、电商金融等研究。

报告包括电子商务方面的重要观点、政策与环境、网购平台、行业应用、电商物流、电商金融等研究。

A green data center is a storage system that stores, manages, and distributes data using various methods that enable maximum energy efficiency with minimal environmental impact. It helps in optimizing the performance of every element in a data center, and lowers the cost of operations associated with a computing system. The complexities of the conventional data center architecture have increased considerably, resulting in higher OPEX and triggering network security concerns. This, coupled with the environmental footprint of data centers using traditional cooling solutions, is fueling the global adoption of green data center solutions. Organizations are incorporating advanced technologies and formulating several strategies to improve overall data center performance. They are using natural cooling solutions, alternate energy technologies, and waste recycling as energy-efficient alternatives compared to conventional data centers.

Digital video refers to videos stored and broadcasted in the digital form, composed of bits, which can be easily stored on a hard disk. It also refers to the data available for distribution and download on electronic media. Digital video content can be broadcasted through four mediums, digital cable TV, IPTV, online video, and VOD. The popularity of smartphones, tablets, and laptops along with the rising penetration of Internet has provided a variety of options for the average consumer to view videos on. In 2014, the Americas accounted for the largest revenue share in the market, followed by EMEA and APAC. The market is predicted to grow at a CAGR of 15.43% during the forecast period. The key leading countries in the market are the US, China, and Japan.

Servers are the backbone of a data center operation, and they are used mainly for the transfer of data. A server operation is dependent on hardware reliability and durability. A typical server consists of dual-power supplies, RAID disk system, and errorcorrecting code (ECC) memory, which are tested for operations by vendors. There have been several innovations in the design and manufacturing of servers, depending on business needs.

Mobile payments enable account holders to pay bills or purchase online, on the go. Before the advent of smartphones, Java-enabled and Internet-based phones were used sparingly because the usage was limited to SMS and calling options, and the carrier charges were high. However, increased adoption of smartphones, tablets, and other mobile technologies has revolutionized the way customers make payments. The transaction volume in the global mobile payment market is expected to grow at a CAGR of 36.26% during the forecast period. By number of users, the market is forecast to grow at a CAGR of 18.10%. EMEA is the leading region in the market, followed by APAC and the Americas.

如何及时处理海量网络态势信息并有效应对动态演化的网络攻击是网络空间安全防御面临的主要挑战,人工智能技术由于具有传统方法所不具备的智能特性,近年来在网络空间安全防御中得到了广泛关注,并取得了大量的研究成果。综述了近年来神经网络、多agent系统以及专家系统等人工智能技术在网络空间安全防御中的主要应用和方法,分析比较了它们各自的应用特点,给出了未来的研究与发展趋势。

彩色图像边缘检测是计算机视觉和图像处理领域的基本课题。详细阐述了彩色边缘检测的方法——基于单色调及矢量的方法,总结了现有方法的优缺点;结合边缘检测性能评价方法分析多种方法的性能,进一步提出了存在的问题及下一步研究方向,为彩色图像边缘检测的研究提供参考和借鉴。

随着服务计算、云计算技术的迅速发展,具有相同或相似功能但服务质量(Qo S)不同的可用服务已大规模部署在互联网上。如何从这些海量Web服务中为服务组合选择令人满意的服务成为一个重要的研究问题,其选择方法是影响组合服务的Qo S和服务组合性能高低的关键因素。为了解决该问题,研究人员开发了许多服务选择方法以帮助用户选择合适的服务。针对近五年基于Qo S的Web服务选择方法的新进展进行了综述,归纳了包括运用Skyline计算、协同过滤、优化算法、多属性决策支持理论和混合方法等一系列典型的服务选择方法,讨论了各方法的优势和局限性;根据当前研究现状,探讨了该领域的发展趋势及未来研究方向和挑战。

为了能在云计算环境下安全地使用可搜索加密技术,针对近几年来可搜索加密技术的研究成果,总结了云计算环境下对称/非对称可搜索加密技术的主要算法模型与安全模型,分析了各类安全方案的安全缺陷,并分析了基本可搜索加密方案与加入了密文安全传输、匿名性、第三方代理、隐私保护协议的四类增强方案在基于选择关键字攻击、字典攻击、统计分析攻击、选择明文攻击四类攻击模型下的安全性。最后,总结了可搜索加密技术的应用陷阱,并提出了当前可搜索加密技术面临的安全性挑战。

Time series analysis is commonly used when monitoring data centers, networks, weather, and even human patients. In most cases, the raw time series data is massive, from millions to billions of data points, and yet interactive analyses require low (e.g., sub-second) latency. Aperture transforms raw time series data, during ingest, into compact summarized representations that it can use to efficiently answer queries at runtime. Aperture handles a range of complex queries, from correlating hundreds of lengthy time series to predicting anomalies in the data. Aperture achieves much of its high performance by executing queries on data summaries, while providing a bound on the information lost when transforming data. By doing so, Aperture can reduce query latency as well as the data that needs to be stored and analyzed to answer a query. Our experiments on real data show that Aperture can provide one to four orders of magnitude lower query response time, while incurring only 10% ingest time overhead and less than 20% error in accuracy.