Projektin yksityiskohdat
Kuvaus (abstrakti)
Contributors: Dennis Ahlfors, Joonas J, Kim Toivonen, Mauri Frestadius, Ossi Räisä, Petja Valkama.
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.
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.
| Lyhennetty nimi | Quer.io |
|---|---|
| Tila | Päättynyt |
| Todellinen alku/loppupvm | 01/09/2018 → 14/12/2018 |