A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics
A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics
复制标题
DOI：
10.1145/3309697.3331491
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Dhruv Kumar;Jian Li;A. Chandra;R. Sitaraman
中科院分区：
文献类型：
--
作者：
Dhruv Kumar;Jian Li;A. Chandra;R. Sitaraman
流数据分析是近年来的一个重要研究课题。随着时间的推移，网络和社交分析、科学计算和能源分析等各种应用领域不断生成大量数据。现代数据分析服务的关键要求之一是实时分析这些数据流，为分析师提取有用且及时的信息。最近开发了几种分布式数据分析平台来满足实时流分析日益增长的需求。如今，在许多流应用程序中，大量数据是由地理分布的源（例如代理、传感器、移动设备、边缘节点等）连续生成的。例如，Facebook、Twitter 和 Netflix 等服务不断从最终用户收集数据，用于各种分析目的，例如查找用户中流行的 Web 内容或监控 QoS 指标。 Akamai 等大型内容交付网络 (CDN) 为互联网上的很大一部分内容提供服务，不断从全球的边缘服务器和客户端收集数据，以了解内容的访问内容、位置和方式，从而为企业提供内容分析见解。
Streaming data analytics has been an important topic of research in recent years. Large quantities of data are generated continuously over time across a variety of application domains such as web and social analytics, scientific computing and energy analytics. One of the key requirements in modern data analytics services is the real-time analysis of these data streams to extract useful and timely information for the analyst. Several distributed data analytics platforms have been developed in recent times to meet this growing requirement of real-time streaming analytics. Nowadays, a large amount of data is generated continuously by geographically distributed sources (e.g., agents, sensors, mobile devices, edge nodes, etc.) in many streaming applications. For instance, services like Facebook, Twitter and Netflix continuously gather data from the end users for a variety of analytical purposes such as finding the popular web content amongst their users or monitoring the QoS metrics. Large content delivery networks (CDNs) like Akamai that serve a significant fraction of content on the Internet continuously collect data from their edge servers and clients from around the globe to understand what, where and how content is accessed for the purpose of providing content analytics insights to businesses.