An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Kafka-ML: connecting the data stream with ML/AI frameworks (now TensorFlow and PyTorch!)

The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.

Explore Apache Kafka data pipelines in Kubernetes.

Learn how to use Kinesis Firehose, AWS Glue, S3, and Amazon Athena by streaming and analyzing reddit comments in realtime. 100-200 level tutorial.

⚙️ Ralph, the ultimate Learning Record Store (and more!) for your learning analytics

unsupervised concept drift detection

In this project, we implemented a topic detection system on Twitter. This system reads tweets from a data stream and assigns them to one of the existing clusters or a new one. Each cluster acts as an agent, which makes the proposed approach a multi-agent system. There is also a coordinator, who monitors the whole system and coordinates the agent.

RPJiOS: RPJ's RPi OS, a sensor data platform for the Raspberry Pi built with python2.7 and redis.

unsupervised concept drift detection with one-class classifiers

This code belongs to ACL conference paper entitled as "An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering"

HyperLogLog and other probabilistic data structures for mining in data streams

Novelty detection for data streams in Python

Algorithms proposed in the following paper: OLIVEIRA, Gustavo HFMO et al. Time series forecasting in the presence of concept drift: A pso-based approach. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. p. 239-246.

Simple cloud based logger for microcontrollers.

Python implementation of the MINAS novelty detection algorithm for data streams.

A low-latency and fault-tolerant framework for Distributed and Deep Neural Networks over the Cloud-to-Things Continuum

A highly-configurable, real-time data quality monitoring tool designed for streaming data

A code generator from high-level formal specifications for monitoring and pattern matching sequential/temporal data.

Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance af…