A python library for quickly estimating database results
Project description
Quer.io
Documentation links
See Documentation for Documentation
See Usage guide for a basic rundown on how to use Quer.io
See Database Schema 1 for single table sample database schema
See Database Schema 2 for normalized sample database schema
See ML documentation for documentation on the machine learning model
Project description
This project is built to the specifications and requirements provided by Prof. Michael Mathioudakis and is a course work project for course TKT20007 Software Engineering Lab at the University of Helsinki, department of Computer Science.
The aim of this project is to build an Approximate Query Processing (AQP) engine -- i.e., a software layer on top of a relational database, that allows us to obtain fast, approximate answers to aggregate queries, with the help of Machine Learning models.
Chosen implementation is a Python library that can be used with multiple different database systems. Machine learning components are built using Scikit Learn.
Installation
This project is done with Python 3.6
See Database Installation guide for information how to install the sample databases this application was tested on.
See Application Installation guide for information how to install the application and all its dependencies.
Optional installation
See Querio Scheduler for how to install and use a scheduler for periodical model retraining.
Tests
Currently the project contains tests that are done using the unittest library. Tests can be run with the following command from the project root
python3 -m unittest discover
This command will find every test from the project and run it. If you want to run an individual test script it can be done with the following command
python3 -m unittest [path to file]
Contributors
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