Delay-Resistant Geo-Distributed Analytics

Bohr: Similarity Aware Geo-distributed Data Analytics

We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more efficiently aggr...

Low Latency Geo-distributed Data Analytics – Public Review

Large cloud service providers ingest massive amounts of data in geographically distributed sites spread across the globe. Analytics for such planetary-scale datasets is an important emerging challenge. The current practice is to copy all data to a central location, where it can be dealt with locally by standard data analytics stacks such as Hadoop and Spark. However, transferring large volumes ...

WANalytics: Analytics for a Geo-Distributed Data-Intensive World

Large organizations today operate data centers around the globe where massive amounts of data are produced and consumed by local users. Despite their geographically diverse origin, such data must be analyzed/mined as a whole. We call the problem of supporting rich DAGs of computation across geographically distributed data Wide-Area Big-Data (WABD). To the best of our knowledge, WABD is not supp...

Geo-Social Media Analytics

Geo-social visual analytics

Spatial analysis and social network analysis typically consider social processes in their own specific contexts, either geographical or network space. Both approaches demonstrate strong conceptual overlaps. For example, actors close to each other tend to have greater similarity than those far apart; this phenomenon has different labels in geography (spatial autocorrelation) and in network scien...