澳大利亚纽卡斯尔代写Essay 特定行业数据分析

2020-02-13 09:00

这篇论文告诉我们数据分析在一个特定的行业，即智能城市中被使用。智能城市是信息和通信技术的集成，也是连接到网络的几种物理设备。现在，随着世界人口的快速增长，因为大多数城市正从城市前形态向城市形态转变。对嵌入式设备(如传感器、驱动器和智能手机)的需求大幅增加，这将为物联网(IoT)的新时代带来潜在的业务。数据分析在整合和收集这些数据方面扮演着非常重要的角色。在这篇研究论文中，我们关注的是分布式智能传感系统和设备如何监控庞大的城市基础设施，以及为什么我们需要自动化控制和收集信息来进行智能决策才能及时做出反应。我们将以这个“智慧城市”产业如何采用数据分析来即兴发挥和发展的形式来讨论这一切。以及数据分析的适应性如何影响城市内的不同活动，比如控制车辆交通、轻松管理停车场、智能家居，这些都涉及到正确管理水和能源消耗及其需求。减少洪水等自然灾害的风险。我们还关注了数据分析在与智慧城市的合作中所面临的主要挑战，并试图阐明未来研究人员提出的解决方案，包括引入自动化系统方法、开发移动计算框架、数据清理和数据收集以及可视化。所有这些解决方案都针对诸如数据隐私、安全性和多样性等问题提出了建议。最后，对结论进行了总结，以期对今后的研究工作有所帮助。
澳大利亚纽卡斯尔代写Essay 特定行业数据分析
The paper tells us about the Data Analytics being used in a specific industry that is smart cities. Smart Cities is an integration of information and communication technology and also the several physical devices that are connected to the network. Now with this rapidly growing population in the world, since most are moving from pre-urban to urban forms. There has been a great increase in the request for embedded devices, such as sensors, actuators, and smartphones that will lead to potential business for the new era of the Internet of Things (IoT).Data Analytics pay a very important role in integrating and collecting this data. In this research paper, we focus on how distributed smart sensing systems and devices monitor the vast urban infrastructure, and why for this to react on time we need automated control and collected information for intelligent decision making. We discuss all of it in the form of how this “smart city” industry has adopted data analytics to improvise and develop. and how the adaption of data analytics impacted different activities within a city like controlling vehicle traffic, Managing Parking lot with ease, Smart homes referring to the proper management of water and energy consumption and its need. Reducing the risk of natural disasters such as Floods. We also focussed on major challenges faced by Data Analytics in its working with smart city and tried to put light on solutions as future research suggested by researchers introducing automated systems methods, developing mobile computing frameworks, data cleaning and data collection along with visualization. All these solutions have been suggested for issues such as Privacy of Data, Security and Variety. At last, we focus on the conclusion to improvise and maintain the current trend of development and work on its future research.

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