Apache Kafka® running on Kubernetes
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Updated
Jul 2, 2024 - Java
Apache Kafka® running on Kubernetes
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
Online anomaly detection for data streams/ Real-time anomaly detection for time series data.
The stream-learn is an open-source Python library for difficult data stream analysis.
BBoxDB is a scalable, highly available, and distributed data store for multi-dimensional big data. The software supports operations like multi-dimensional range queries and spatial joins. In addition, data streams are supported.
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
AutoML framework for implementing automated machine learning on data streams
Uber-Netflix-Microservices-Architrecture-Using-Rest-Kafka-Eureka-Zuul
ActiSiamese (Neurocomputing 2022)
Queue-Based Resampling (QBR, ICANN 2018)
A React group chat demo powered by PubNub and reusable chat components.
Methods for Finding Frequent Items in Data Streams
Python package for synchronization of data streams or sensor systems employing IMU data
Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04
Easy to integrate and powerful Oracle on top of Chainlink Data Streams and Chainlink Data Feeds
Implementation of "Fast clustering-based anonymization approaches with time constraints for data streams".
This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"
Adaptive REBAlancing (AREBA, IEEE TNNLS 2021)
Augmented Queues (IEEE SSCI 2022)
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