The Titan Control Center is a scalable and extensible analytics platform for Industrial DevOps. It analyzes and visualizes data streams from Internet of Things (IIoT) sensors (e.g. electrical power consumption) in industrial production.
This repository contains necessary infrastructure for deploying the Titan Control Center with Docker Compose or in a cloud environment operated by Kubernetes.
The Titan Control Center is designed in a microservice-based architecture augmented by big data and stream processing techniques. The individual software components of the Titan Control Center are located in separate Git repositories:
- History The History microservice
- Aggregation The Aggregation microservice
- Statistics The Statistics microservice
- Sensor Management The Sensor Management microservice
- Anomaly Detection The Anomaly Detection microservice
- Visualization Web-based Visualization and the corresponding API gateway
- Common Library code and record definitions to be used by all microservices
- Titan Platform The Titan Platform, which is used for data stream aggregation and further interpretation
Before deploying the Titan Control Center, simply clone this repository or download one of our releases. We provide deployment declarations for Docker Compose and Kubernetes. While Docker Compose is a good fit having a quick look into the software, we highly recommend using Kubernetes for more serious deployments. Instructions for both options can be found in their corresponding directories:
- Deploying the Titan Control Center with Docker Compose
- Deploying the Titan Control Center with Kubernetes
The documentation of the Titan Control Center can be found in the docs folder. It includes generel usage instructions for users, developers and operators.
Please cite the Titan Control Center as follows:
S. Henning, W. Hasselbring, The Titan Control Center for Industrial DevOps analytics research, Software Impacts 7 (2021), DOI: 10.1016/j.simpa.2020.100050.
BibTeX:
@article{Henning2021,
title = {The Titan Control Center for Industrial DevOps analytics research},
journal = {Software Impacts},
volume = {7},
pages = {100050},
year = {2021},
doi = {10.1016/j.simpa.2020.100050},
author = {Sören Henning and Wilhelm Hasselbring},
}