Collaboration spotting X (CSX) is a network-based visual-analytics application for exploring tabular data through network visualizations, interactions, and advanced network analytics. The core idea of CSX is to enable users to retrieve a subset of their dataset and provide tools for visual and interactive exploration and filtering of their retrieved data. You can also view a presentation video on Collaboration spotting on the following link.
This project was developed by Aleksandar Bobić as part of his PhD research during his time as a doctoral student at CERN and the Graz University of Technology under the supervision of his CERN supervisor Jean-Marie Le Goff and his TU Graz supervisor Christian Gütl.
This project was inspired by concepts introduced in the previous Collaboration Spotting project. We would like to thank the previous Collaboration Spotting team for their contributions.
If you would like to collaborate or contribute to the project or have any questions feel free to send me an email to aleksandar.bobic@cern.ch.
Contributors from the following institutions were involved in the development of this project:
If you happen to mention or use this project as part of one of your scientific works, please cite the following paper: Bobic, A., Le Goff, J. M., & Gütl, C. (2021). Collaboration Spotting X-A Visual Network Exploration Tool. In in Proceedings of the The Eighth International Conference on Social Networks Analysis, Management and Security: SNAMS 2021.
- Bobic, A., Le Goff, J. M., & Gütl, C. (2021). Towards supporting complex retrieval tasks through graph-based information retrieval and visual analytics. In CEUR Workshop Proceedings (Vol. 2950, pp. 30-37). RWTH Aachen. Presentation Video
- Bobic, A., Le Goff, J. M., & Gütl, C. (2021). Collaboration Spotting X-A Visual Network Exploration Tool. In in Proceedings of the The Eighth International Conference on Social Networks Analysis, Management and Security: SNAMS 2021. Presentation Video
To start developing this project, please complete the following steps:
- Install docker on your local machine
- Install python 3.7 or higher on your local machine
- Clone the CSX project to your machine
- Start docker
- In a terminal, navigate to the project directory and run
docker-compose up, which will start the app in development mode. - Once the project is running, it will be accessible on http://localhost:8882
- Before you can start exploring the example dataset, open another terminal and navigate to the
datasets_examplefolder in the project folder - Run the following command in python
python populate_elastic.py jucssm jucs_sm.ndjsonto populate the CSX storage with the sample dataset - Refresh your browser page and start exploring
Here is an example video showing how to use CSX once the example data has been loaded. It can be roughly divided into the following sections:
- Performing an initial search on your dataset
- Expressing a complex information need through the workflow designer
- Exploring the overview network
- Modifying the network schema
- Exploring the detail network
If you want to contribute to this project, pick an open issue you find interesting and create your branch (from the develop branch) with the issue number as the branch name. If there is no open issue for your feature, please open a new issue with a detailed description of the feature first.
Once you are happy with your implementation, open a pull request to the develop branch.
Run docker-compose up, which will start the app in development mode.
Navigate to the dataset_examples in your terminal and run the following command to populate the running elastic instance with sample data collected from the Journal of Universal Computer Science: python populate_elastic.py jucssm jucs_sm.ndjson. The first argument is the index name, and the second argument is the file name.
You can drop your custom dataset in the same folder and run the command by changing the parameters for the index name and the file name. The custom dataset should have the following format:
{"index": {"_id": 0}}
{"Column 1": "Value 1.1", "Column 2": "Value 2.1", "Column 3": ["Value 3.1.1", "Value 3.1.2"]}
{"index": {"_id": 1}}
{"Column 1": "Value 1.2", "Column 2": "Value 2.2", "Column 3": ["Value 3.2.1", "Value 3.2.2", "Value 3.2.3"]}A file containing the config for this dataset can be found in the server/app/data/config folder. This file can be used to modify your default config for the uploaded dataset. If you add more datasets, please make sure to name the config file of each new dataset the same as the index. Dataset config files are necessary for each dataset!
The config file has the following parameters:
{
"default_visible_dimensions": ["feature 1", "feature 2"], // Default visible features in detail network
"anchor": "featur 2", // Default anchor node for both networks
"links": ["feature 1"], // Default link features for both networks
"default_search_fields": ["feature 2"], // Features used for searching through the start page searchbar
"schemas": [ // Default schemas (can be left empty)
{
"name": "schema name",
"relations": [
{
"dest": "feature 1",
"src": "feature 2",
"relationship" "oneToOne" // Can be oneToOne, oneToMany, ManyToOne and manyToMany
},
{
"dest": "feature 2",
"src": "feature 3",
"relationship" "oneToMany"
}
]
}
]
}Open http://localhost:8882 to view CSX in the browser and explore the newly added dataset.
Run docker-compose -f docker-compose.prod.yml up --build --remove-orphans
Runs the app in production mode. Open http://localhost:8880 to view it in the browser.