A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and output...
The dominant model for computing with large-scale data in cloud environments has been founded on batch processing including the Map-Reduce model. Important use-cases such as monitoring and alerting in the cloud require instead the incremental and continual handling of new data. Thus recent systems ...
Focusing on the core technologies of real-time stream computing platform in cloud environment, this paper conducts a series of researches and implementation of the system. First of all, aiming at the availability of real-time streaming computing platform, we design a high availability framework ...
Siddhi is an open source, lightweight, stream processing and complex event processing engine. - Siddhi - Cloud Native Stream Processor
What are the stream processing frameworks? Spark, Flink and Kafka Streams are the most common open source stream processing frameworks. In addition, all the primary cloud services also have native services that simplify stream processing development on their respective platforms, such as Amazon Kinesis...
Learn how to build an end-to-end reactive stream processing application using Apache Kafka as an event streaming platform, Quarkus for your backend, and a frontend written in Angular. Product Page Streams for Apache Kafka A lightweight, high-performance, robust, event streaming platform. Articl...
A real-time stream processing framework must be able to process messages "in-stream" without having to store them on disk, which adds unacceptable latency on the critical path. Additionally, these systems should be active (event driven) and not passive (whereby applications need to poll the res...
Cloud computing adds great on-demand scalability to stream processing systems with its pay-per-use cost model. However, to promise service level agreements to users while keeping resource allocation cost low is a challenging task due to uncertainties coming from various sources, such as the target...
The paper "Stream- Processing Points" by Renato Pajarola shows a way to design a framework which allows to process a subset of very large point clouds... C Scheiblauer,M Wimmer 被引量: 3发表: 2011年 A Review of Dynamic Scalability and Dynamic Scheduling in Cloud-Native Distributed Stream ...
Analytics Anywhere – AI from the Cloud to the EdgeWhat if you could analyze your data as it was received, no matter where it originated? SAS Event Stream Processing provides analytics where you need it – from the cloud to the edge....