Title: A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics
Abstract: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 ...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.Read More