Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Enabling real time data analysis
Proceedings of the VLDB Endowment
Efficient event processing through reconfigurable hardware for algorithmic trading
Proceedings of the VLDB Endowment
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Towards highly parallel event processing through reconfigurable hardware
Proceedings of the Seventh International Workshop on Data Management on New Hardware
Towards vulnerability-based intrusion detection with event processing
Proceedings of the 5th ACM international conference on Distributed event-based system
Towards an extensible efficient event processing kernel
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
Pub/Sub on stream: a multi-core based message broker with QoS support
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
The power of software-defined networking: line-rate content-based routing using OpenFlow
Proceedings of the 7th Workshop on Middleware for Next Generation Internet Computing
Tutorial: event-based systems meet software-defined networking
Proceedings of the 7th ACM international conference on Distributed event-based systems
Hi-index | 0.01 |
In this demo, we present fpga-ToPSS (a member of Toronto Publish/Subscribe System Family), an efficient event processing platform for high-frequency and low-latency algorithmic trading. Our event processing platform is built over reconfigurable hardware---FPGAs---to achieve line-rate processing. Furthermore, our event processing engine supports Boolean expression matching with an expressive predicate language that models complex financial strategies to autonomously mimic the buying and the selling of stocks based on real-time financial data.