Sensorbee is designed for low-latency processing of streaming data at the edge of the network. IoT devices frequently generate large volumes of unstructured streaming data, such as video and audio streams. Even if the data streams are structured, they may be meaningless if their temporal characteristics are not considered. Cloud-based services are generally not good at processing these kinds of data. Preprocessing data streams before they are sent to the cloud makes large scale data processing in the cloud more efficient and reduces the usage of network bandwidth. 
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Sensorbee extracts useful information from unstructured data often requires machine learning. It works well with online machine learning algorithms by storing machine learning models as user-defined states. It supports various machine learning toolkits, including Chainer and some components of Jubatus. In addition, SensorBee’s query language enables stateful temporal data handling.

Sensorbee is designed for low-latency processing of streaming data at the edge of the network. IoT devices frequently generate large volumes of unstructured streaming data, such as video and audio streams. Even if the data streams are structured, they may be meaningless if their temporal characteristics are not considered. Cloud-based services are generally not good at processing these kinds of data. Preprocessing data streams before they are sent to the cloud makes large scale data processing in the cloud more efficient and reduces the usage of network bandwidth. 

Sensorbee - Lightweight stream processing engine for IoT

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