Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Speculative out-of-order event processing with software transaction memory
Proceedings of the second international conference on Distributed event-based systems
Distributed event stream processing with non-deterministic finite automata
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
Results on out-of-order event processing
PADL'11 Proceedings of the 13th international conference on Practical aspects of declarative languages
Efficient event stream processing: handling ambiguous events and patterns with negation
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Complex pattern ranking (CPR): evaluating top-k pattern queries over event streams
Proceedings of the 5th ACM international conference on Distributed event-based system
Proceedings of the 7th ACM international conference on Distributed event-based systems
A query-matching mechanism over out-of-order event stream in IOT
International Journal of Ad Hoc and Ubiquitous Computing
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Complex event processing has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. The goal is to extract patterns from such event streams in order to make informed decisions in real-time. However, networking latencies and even machine failure may cause events to arrive out-of-order at the event stream processing engine. In this work, we address the problem of processing event pattern queries specified over event streams that may contain out-of-order data. First, we analyze the problems state-of-the-art event stream processing technology would experience when faced with out-of-order data arrival. We then propose a new solution of physical implementation strategies for the core stream algebra operators such as sequence scan and pattern construction, including stack-based data structures and associated purge algorithms. Optimizations for sequence scan and construction as well as state purging to minimize CPU cost and memory consumption are also introduced. Lastly, we conduct an experimental study demonstrating the effectiveness of our approach.