Incremental stream processing using computational conflict-free replicated data types
Proceedings of the 3rd International Workshop on Cloud Data and Platforms
StreamHub: a massively parallel architecture for high-performance content-based publish/subscribe
Proceedings of the 7th ACM international conference on Distributed event-based systems
Adaptive online scheduling in storm
Proceedings of the 7th ACM international conference on Distributed event-based systems
Tutorial: Elastic and Fault Tolerant Event Stream Processing using StreamMine3G
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
Scalable and Real-Time Deep Packet Inspection
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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We present StreamMapReduce, a data processing approach that combines ideas from the popular MapReduce paradigm and recent developments in Event Stream Processing. We adopted the simple and scalable programming model of MapReduce and added continuous, low-latency data processing capabilities previously found only in Event Stream Processing systems. This combination leads to a system that is efficient and scalable, but at the same time, simple from the user's point of view. For latency-critical applications, our system allows a hundred-fold improvement in response time. Notwithstanding, when throughput is considered, our system offers a ten-fold per node throughput increase in comparison to Hadoop. As a result, we show that our approach addresses classes of applications that are not supported by any other existing system and that the MapReduce paradigm is indeed suitable for scalable processing of real-time data streams.