Big data analytics pose several challenges for modern databases. One key such
challenge arises from the streaming nature of big data, which mandates efficient
algorithms for querying and analyzing massive, continuous data streams with
limited memory and CPU-time resources. Such streams arise naturally in emerging
large-scale event monitoring applications. In addition to memory- and
time-efficiency concerns, the inherently distributed nature of such applications
raises important 
communication-efficiency issues, making it critical to carefully optimize the
use of network resources. In this talk, we introduce the distributed data
streaming model, and discuss some of our work on tracking complex queries over
massive distributed streams, as well as new research directions in this space.