Desbordante user guide
This manual explores the main Desbordante concepts and navigates through the general usage scenario.
Try the deployed instance
Table of Contents
- General
1. Choosing the profiling task
2. Choosing the dataset
3. Task-dependent analysis - Other features
- Cite
- Contacts
General
Desbordante is a data profiling tool. Its general usage scenario consists of five steps:
- Choose a profiling task.
- Choose the dataset.
- Configure the task.
- Run1 the task.
- Analyze the results.
1Note that tasks are computationally expensive. Due to possible heavy workload, the deployed version has memory and run time limits for all users. If your task takes up more than 512MB RAM or 30 seconds of runtime, it will be killed. Contact us if you are interested in raising the limit.
The current section covers the first two steps, while the remaining ones are specific for each profiling task.
Choosing the profiling task
Pick one of the existing tasks.
Choosing the dataset
Desbordante provides a built-in collection of datasets to test its functionality. It is not possible for unauthorized users to upload their own data, so you will need to sign up to be able to do so. After filling in the form you will only need to verify the provided email address.
Choose an out-of-the-box dataset or upload your own. Currently, Desbordante accepts only .csv files.
Task-dependent analysis
Check out the corresponding profiling task guide to continue your analysis:
- mining Functional Dependencies;
- mining Conditional Functional Dependencies;
- mining Association Rules;
- employing the Error Detection Pipeline.
Other features
- You can return to the main screen at any time by clicking the Desbordante logo or title.
- You can share the profiling results after the task execution finishes by copying the url with the task id.
- We would really appreciate reviews and opinions on our tool’s usability and usefulness. We would be grateful if you provided your feedback using a dedicated form which can be accessed via the button on the right.
Cite
If you use this software for research, please cite the paper (https://fruct.org/publications/fruct29/files/Strut.pdf, https://ieeexplore.ieee.org/document/9435469) as follows:
M. Strutovskiy, N. Bobrov, K. Smirnov and G. Chernishev, “Desbordante: a Framework for Exploring Limits of Dependency Discovery Algorithms,” 2021 29th Conference of Open Innovations Association (FRUCT), 2021, pp. 344-354, doi: 10.23919/FRUCT52173.2021.9435469.