Efficiently Correlating Complex Events over Live and Archived Data Streams

Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
Correlating complex events over live and archived data streams, which the authors call Pattern Correlation Queries (PCQs), provides many benefits for domains which need real-time forecasting of events or identification of causal dependencies, while handling data at high rates and in massive amounts, like in financial or medical settings. Existing work has focused either on complex event processing over a single type of stream source (i.e., either live or archived), or on simple stream correlation queries (e.g., live events trigerring a database lookup). In this paper, they specifically focus on recency-based PCQs and provide clear, useful, and optimizable semantics for them.

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