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Brain4it is an open source platform to develop artificial intelligence applications for the Internet of things.

Home Page: http://brain4it.org

License: Other

Java 50.12% HTML 5.86% CSS 1.30% JavaScript 42.25% Shell 0.03% Batchfile 0.02% C++ 0.42%
iot artificial-intelligence java homoiconic

brain4it's Introduction

brain4it.org: a brain for the IoT

Brain4it is an open source platform to develop artificial intelligence applications for the Internet of things.

It runs as a network service that provides a REST API to remotely control and program it using a simple yet powerful functional language that facilitates the implementation of expert systems and machine learning applications.

Brain4it can be installed in a wide range of devices, from smartphones and embedded systems to cloud servers, which expands its possibilities and use cases.

It is specially indicated for the management of dynamic environments where flexibility and adaptability are essential requirements.

Brain4it has proven its value in multiple areas: smartcities, domotics, robotics, IT infraestructure management, mathematics, etc.

More information: http://brain4it.org

brain4it's People

Contributors

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brain4it's Issues

New BPL library for Kafka

In Sant Feliu we are planning to use Kafka as a central bus for the smart city stack. In order to publish and read to/from this bus comfortably, we should create a new BPL library similar to the XMPP and MQTT ones.

As Kafka is written in Java, we can use the official client.

Kafka includes five core apis:

  • The Producer API allows applications to send streams of data to topics in the Kafka cluster.
  • The Consumer API allows applications to read streams of data from topics in the Kafka cluster.
  • The Streams API allows transforming streams of data from input topics to output topics.
  • The Connect API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application.
  • The Admin API allows managing and inspecting topics, brokers, and other Kafka objects.

Before starting to implement, install Kafka and try a simple usecase: create topic, register a subscription, and publish to the topic. Identify which api calls we need for this usecase.

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