#Open Dataset Inspector (ODIN) This is a tool used for exploration and evaluation of various search methods for finding datasets in open data portals. See documentation for more details.
Currently, it serves three main tasks.
In this part of the tool, for a given set of input datasets, search results of various search methods are presented. The user does not see the identification of the method producing particular results. The results can be ordered by the user based on their percieved relevancy to the given use case. The ordering is collected for further processing with the aim of determining the performance of individual search methods when used in various use cases and by various users.
In the exploration part, the user can input their datasets and see the search results, including identification of the methods giving particular results. This use case is for exploring the search methods and the results they give, with no intent of collecting the results.
This part of the tool is used to explore dataset similarity based on theri mapping to a subgraph of Wikidata made of instance of and subclass of predicates. The tool offers three kinds of graph visualizations.
THe whole tool consists of four projects: data-preparation, evaluation, odin-backend , and odin-frontend.
Based on the use-case different components need to be running/executed.
This project contains scripts that are responsible for data preparation. Please navigate to this directory and use scripts that generate data that you need to run the other components.
Used to process results of user evaluation done by odin-frontend.
- Make sure you have Python (3.5+) installed
- Make sure you have evaluation files ready in
./data/evaluation - Install dependencies using
pip install numpy plotly - Install Orca for plotly
- Update arguments in the scripts
evaluate.pyandplot_graphs.py - Run the updated script.
Provide functionality to compute graph-based dataset similarity. Can be run as a command line tool or as a web service. The web service is required by odin-frontend in order to visualize similarity details in Visualisation. It requires data prepared by data-preparation project.
- Make sure all data are prepared by data-preparation.
- Make sure you have Python (3.5+) installed
- Install dependencies using
pip install flask pyyaml - Navigate into project directory
- Start the service by
python webserver.py
- Make sure you have evaluation files ready in
./data/evaluation - Make sure you have NodeJs installed
- Navigate to project directory
- Run
npm cito install packages - Run
npm run buildto build the project - Run
npm run startto start the web server