Keywords: Online monitoring of data streams poses a challenge in many data-centric applications including network traffic management, trend analysis, web-click streams, intrusion detection, and sensor networks. Indexing techniques used in these applications have to be time and space efficient while providing a high quality of answers to user queries: (I) queries that monitor aggregates, such as finding surprising levels ("volatility " of a data stream), and detecting bursts, and (2) queries that monitor trends, such as detecting correlations and finding similar patterns. Data stream indexing becomes an even more challenging task, when we take into account the dynamic nature of underlying raw data. For example, bursts of events can occur at variable temporal modalities from hours to days to weeks. We focus on a multi-resolution indexing architecture. The architecture enables the discovery of "interesting " behavior online, provides flexibility in user query definitions, and interconnects registered queries for real-time and in-depth analysis. stream indexing, monitoring real-time systems, mining continuous data flows, multi-resolution index, synopsis maintenance, trend analysis, network traffic
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