Commit cc64d09a authored by Domenico Giordano's avatar Domenico Giordano
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review README

parent 191135d4
......@@ -16,30 +16,30 @@ Some of the functionalities supported are:
A central part of this project is the Anomaly Detection on time series data.
This time series data can come from:
The time series data can come from:
- metrics measured for each hypervisor in the Data Centre.
- derived timeseries from log file analysis.
The CI/CD of this project is used to
The CI/CD of this project is used to:
1. Run unit tests and quality checks for the implemented code
1. Build Docker images with pre-installed libraries needed for the project's scope
1. Run functional tests of the Data Analytics' pipeline, and its components
The repository contains extensive documentation of each subfolder in the README file included in the specific subfolder.
The repository contains extensive documentation of each subfolder in the README file included in the specific subfolder.<br>
This is a guide map of the repository:
1. ETL libraries ([link](etl))<br>
1. ETL libraries ([etl](etl))<br>
Implement the extraction of data from the different monitoring databases: InfluxDB, ElasticSearch, HDFS
1. Tests suite ([link](tests))<br>
1. Tests suite ([tests](tests))<br>
Unit tests of the ETL libraries, test pipelines' components
1. Javascript Grafana extension ([link](grafana_extension))<br>
1. Javascript Grafana extension ([grafana_extension](grafana_extension))<br>
Implement an extension of the Grafana Annotation panel, modifying the Grafana JS code
1. Anomaly detection libraries ([link](adcern))<br>
1. Anomaly detection libraries ([adcern](adcern))<br>
Implement anomaly detection Models, based on pyOD, traditional ML and DL methods
1. Docker image definition ([link](docker-images))<br>
1. Docker image definition ([docker-images](docker-images))<br>
Dockerfiles for images used in this project
1. Airflow-based Anomaly Detection System ([link](control_room))<br>
1. Airflow-based Anomaly Detection System ([control_room](control_room))<br>
Setup and run the Anomaly Detection System
## From where to start
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