Approximate Geometric Query Tracking over Distributed Streams

Effective Big Data analytics pose several difficult challenges for modern data management architectures. One key such challenge arises from the naturally streaming nature of big data, which mandates efficient algorithms for querying and analyzing massive, continuous data streams (that is, data that is seen only once and in a fixed order) with limited memory and CPU-time resources. Such streams arise naturally in emerging large-scale event monitoring applications; for instance, network-operations monitoring in large ISPs, where usage information from numerous sites needs to be continuously collected and analyzed for interesting trends. In addition to memoryand time-efficiency concerns, the inherently distributed nature of such applications also raises important communication-efficiency issues, making it critical to carefully optimize the use of the underlying network infrastructure. In this paper, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as the combination with streamsketching tools and local prediction models, as well as more recent developments leading to a more general theory of Safe Zones and interesting connections to convex Euclidean geometry. Finally, we outline various challenging directions for future research in this area.