Improving Big Data Infrastructure to Effectively Utilize Resources for Data Processing and Services

完善大数据基础设施，有效利用数据处理和服务资源

• 批准号: RGPIN-2015-05390
• 负责人: Shi, Wei
• 金额: $ 1.31万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634464
• 关键词: Improving; Big; Data; Infrastructure; Effectively

Big data analytics is hailed as an emerging driving force of the next innovation wave. It has formed the basis for many novel applications across a wide range of fields from business workflows to engineering activities and scientific computing. One such typical application is cloud-based network services for business and individual mobile or vehicular users, where data is collected through daily log files, from stationary or mobile entities such as smartphones equipped with a variety of built-in sensors (e.g., GPS, accelerometers and cameras) and then processed by a big-data infrastructure in order to provision data as a service. For example, vehicle trajectories can be generated in real time from video data to facilitate the analysis of traffic flow parameters for transportation management. Clearly, in such applications, the response time of the underlying infrastructure for data processing has great impact on Quality of Service (QoS).

大数据分析被誉为下一波创新浪潮的新兴驱动力。它已经成为从业务工作流程到工程活动和科学计算等广泛领域的许多新应用程序的基础。一个这样的典型应用是用于商业和个人移动的或车辆用户的基于云的网络服务，其中通过日常日志文件从固定或移动的实体（诸如配备有各种内置传感器（例如，GPS、加速度计和摄像头），然后由大数据基础设施进行处理，以提供数据即服务。例如，可以从视频数据中真实的实时生成车辆轨迹，以便于分析交通流参数以进行运输管理。显然，在这样的应用程序中，底层基础设施的数据处理的响应时间对服务质量（QoS）有很大的影响。