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。

Electronic commerce, commonly called e-commerce, refers to the process of buying and selling products and services over computer networks. Several industry verticals, such as traditional brickand- mortar retailers, banks, travel agents, and online vendors, have aggressively embraced ecommerce. This technology helps them operate through web portals, gain competitive advantage, and reach more customers faster. E-commerce software offers advanced capabilities such as searching, order management, cart management, content management, marketing, and reporting. Further, the integration of online technologies with smartphones and social media networks has expanded the market's reach.

Migrating a business's operations to the cloud is a complex process that requires consultation and step-by-step implementation.

E-commerce uses an electronic network to conduct buying and selling of goods and services for an organization or an individual. E-commerce space has exhibited significant dynamism with the advent of technology, changing demographics, and process improvements in supply chain model. The growth of e-commerce is transforming the market landscape of several industry verticals. It has influenced the entire ecosystem of societies, the way of living and products' consumption with widespread adoption of digital technologies. Companies are focusing on logistics and SCM to deliver best customer experience and increase brand value for retailers. Use of IT in the e-commerce industry chain has enabled fast and efficient flow of data exchange, boosting productivity.

Enterprises worldwide have become increasingly dependent on IT and the Internet to run their businesses efficiently and drive revenue growth. The role of IT departments has become vital and directly contributes to an organization’s agenda. As a result, IT departments are taking effective measures to ensure smooth operations. One of the key responsibilities of CIOs in organizations is to provide IT infrastructure that is agile, resilient, secure, and cost-effective.

针对物流云服务模式中调度任务多、信息量大、需求广的特点,提出了一种改进蝙蝠算法求解物流云服务调度问题的方案,其优化目标为最小化调度时间和最大化资源利用率。根据设计的算法流程,首先基于工件升序排列(ranked order value,ROV)规则对蝙蝠个体进行重新编码;然后调整初始化数据范围来减少分配任务超载和资源闲置现象,并在迭代过程中增加约束条件来均衡任务量,最终实现了资源与任务的智能调度。通过和遗传、粒子群以及基本蝙蝠算法的对比分析,体现了改进算法的优越性。最后利用Witness对方案进行仿真,证明了改进蝙蝠算法在解决物流云服务任务调度中的有效性,同时扩展了蝙蝠算法的应用领域。

针对回溯搜索优化算法收敛速度慢和易早熟的缺点,提出了一种改进算法。首先,利用麦克斯韦分布产生变异尺度系数,并在此基础上提出了一种新的变异算子。新变异算子有效地加快了收敛速度。同时,在变异策略中添加了一种选择机制以增加全局搜索能力,避免出现早熟收敛。通过与差分进化的变异策略对比和经典测试函数的测试,实验结果表明改进算法不仅具有较快的收敛速度,而且具有良好的全局搜索能力。

提出一种基于词项关联关系与归一化割加权非负矩阵分解的微博用户兴趣模型构建方法。该方法首先基于词分布上下文语义相关性来建立词项关联关系矩阵刻画词项间相似度,然后应用归一化割加权非负矩阵分解算法获取用户—主题矩阵,产生用户感兴趣的微博主题聚类结果。实验表明,此方法能有效地进行微博主题聚类,并支持微博用户兴趣模型构建。

针对模糊推理航迹关联算法隶属度函数确定较为繁琐、影响算法灵活性等现实问题,将证据理论引入模糊航迹关联算法,对隶属度函数进行加权处理以获取证据信息,并在此基础上进行基于证据理论的融合处理。仿真分析表明,基于模糊理论的证据航迹关联算法具有较强的环境适应性,能够在不大幅增加运算量的情况下进一步降低模糊航迹关联的不确定性,体现了证据理论在解决不确定问题上的优良特性。

In this thesis, we investigate the switching dynamics in thin film ferroelectrics. We have developed a phase field model that describes the physics of polarization dynamics at the microscopic scale. A number of efficient numerical methods have been applied for achieving massive parallelization of all the calculation steps. Conformally mapped elements, node wise assembly and prevention of dynamic loading minimized the communication between processors and increased the parallelization efficiency. With these improvements, we have reached the experimental scale - a significant step forward compared to the state of the art thin film ferroelectric switching dynamics models. Using this model, we elucidated the switching dynamics on multiple surfaces of the multiferroic material BFO. We also calculated the switching energy of scaled BFO islands. Finally, we studied the interaction of domain wall propagation with misfit dislocations in the thin film.

Shark is a research data analysis system built on a novel coarse-grained distributed shared-memory abstraction. Shark pairs query processing with deep data analysis, providing a unified system for easy data manipulation using SQL and pushing sophisticated analysis closer to its data. It scales to thousands of nodes in a fault-tolerant manner. Shark can answer queries over 40 times faster than Apache Hive and run machine learning programs on large datasets over 25 times faster than equivalent MapReduce programs on Apache Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, with the fine-grained fault tolerance properties that such an engine provides. Shark additionally provides several extensions to its engine, including table and column-level statistics collection as well as a cost-based optimizer, both of which we describe in depth in this paper. Cost-based query optimization in some cases improves the performance of queries with multiple joins by orders of magnitude over Hive and over 2 times compared to previous versions of Shark.