Complex-Event Mining Over Centralized and Distributed Data Streams

Massive, continuous data streams arise naturally in emerging large-scale event monitoring scenarios (such as enabling observability for complex distributed systems, or network-operations monitoring in large ISPs), where usage information from numerous devices needs to be continuously collected and analyzed for interesting trends and real-time reaction to different conditions (e.g., anomalies, hotspots, or DDoS attacks). Such applications raise important memory-, time-, and communication-efficiency issues, making it critical to carefully optimize the use of available computation and communication resources. In this talk, I will provide an overview of centralized and distributed data streaming models and some of the key algorithmic tools in the space of streaming complex-event mining, along with relevant applications and directions for future research.