Stream Analyze | Go Further

Publications

Discover how our innovative technology is pushing the edge of what is possible.

10.6.2024

SA Engine Overview

SA Engine, the kernel system powering the Stream Analyze platform, has a highly optimized architecture making it independent of operating systems and other software, resulting in an extremely small and efficient footprint. This whitepaper describes the architecture of SA Engine.

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14.6.2023

Stream Analyze – Making stupid things smart

Discover the game-changing potential of edge computing in industries. Real-time insights, improved efficiency, and customer experiences await. Explore the benefits and use cases in this insightful ebook.

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20.2.2023

Deploying ultra-low power machine learning at the edge

This white paper by the tinyML® Foundation explores challenges and solutions in deploying ultra-low power ML at the edge, including a practical case study from Stream Analyze.

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Research

Innovating the future in edge analytics.

From Publication to Production: Interactive Deployment of Forklift Activity Recognition

12.4.2024

This paper demonstrates the deployment of machine learning (ML) models on a real forklift truck and the utilization of an interactive method that essentially bridges the gap between laboratory and realistic settings of the forklift application.

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Massive scale-out of expensive continuous queries

8.1.2011

A parallelized stream splitting operator enabling scalable execution of expensive continuous queries over high-volume data streams, achieving network-bound maximum stream rates and energy efficiency, with demonstrated scalability on the Linear Road Benchmark.

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Scalable validation of industrial equipment using a functional DSMS

8.16.2016

A stream validation system called SVALI is developed in order to continuously validate correct behavior of industrial equipment.

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Scalable Real-Time Analytics for IoT Applications

10.8.2021

Combining edge data stream management system (EDSMS) and NoSQL database MongoDB for scalable analytics in large-scale IIoT applications, enabling real-time and historical analytics and supporting advanced analytics through extended query processing (EQP) system.

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Analytics of IIoT Data Using a NoSQL Datastore

10.8.2021

Comparative analysis of RDBMS and NoSQL approaches in distributed IIoT applications, integrating edge computing to overcome scalability, heterogeneity, and limitations in advanced analytics.

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