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AI Watch B1: Streaming anomaly detection on streaming data given by Digital Twin through Eclipse Ditto using Apache Kafka.

License: Apache License 2.0

Python 56.15% JavaScript 9.28% Shell 0.07% HTML 34.50%
docker json pynomaly python pysad zookeeper python-confluent-kafka eclipse-ditto

ai_watch_b1's Introduction

AI Watch B1

AI Watch B1 is a module of the AiWatch project that includes the implementation of the digital twin and software for detecting outliers on an infinite data stream. Eclipse Ditto receives the coordinates from the Ai Watch A1 module, updates the digital twin and send them to the Streaming Anomaly Detector. This software sends the coordinates with the "anomaly_value" flag to the Ai Watch C1 (Unity 3D) module.

Demo AI WATCH ๐Ÿ’ป

  • [Video Unity and anomaly (when avatar color = red) output]
aiwatch.output.mp4

Anomaly result values shown in the video:

System Structure ๐Ÿ›

Docs ๐Ÿ“œ

  • AI Watch B1 documentation was generated using Doxygen

Installation ๐Ÿš€

this AiWatch module includes two main software: Eclipse Ditto (implementation of Digital Twin) and Streaming Anomaly Detector, so the installation and setup is divided into two parts to be followed in the following order:


  1. SETUP/RUN Apache Kafka and Eclipse Ditto and SETUP ECLIPSE DITTO FOR AIWATCH PROJECT
  2. SETUP/RUN Streaming Anomaly Detector

The system was mainly used on the following hardware:

But a test was also carried out correctly on ubuntu 20.4 LTS. Instead I do not recommend using Mac computers with M1 or M2 processor for the first test.

Tools ๐Ÿ› 

License โ˜ข๏ธ

AI Watch B1 is licensed under the Apache License, Version 2.0. Copyright 2022. Please, see the license.

Contacts ๐Ÿชช

  • [mail] luca [ dot ] rubino001 [ at ] studenti [ dot ] uniparthenope [ dot ] it (you can write to me in english or italian).

Citation ๐Ÿ“–

    @report{AIWatchB1,
        author = {Luca Rubino},
        title = {Streaming anomaly detection on streaming data given by Digital Twin through Eclipse Ditto using Apache Kafkaโ€‹},
        institution = {University of Naples, Parthenope},
        year = {2022}
    }

Supervisor

Other modules

a.a. 2021/2022

ai_watch_b1's People

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