Result: SCALAR - A Platform for Real-time Machine Learning Competitions on Data Streams

Title:
SCALAR - A Platform for Real-time Machine Learning Competitions on Data Streams
Authors:
Radulovic, Nedeljko, orcid:0000-0001-5056-, Boulegane, Dihia, Bifet, Albert
Contributors:
Radulovic, Nedeljko, Boulegane, Dihia, Bifet, Albert, Nenad Stojanovic
Publisher Information:
Zenodo
Publication Year:
2020
Collection:
Zenodo
Subject Terms:
Stream Data Mining, Real-time Machine Learning, Information Flow processing, Python
Document Type:
Electronic Resource software
Language:
English
Relation:
https://zenodo.org/records/4299807; oai:zenodo.org:4299807; https://doi.org/10.5281/zenodo.4299807
DOI:
10.5281/zenodo.4299807
Availability:
https://doi.org/10.5281/zenodo.4299807
https://zenodo.org/records/4299807
Rights:
Apache License 2.0 ; apache-2.0 ; http://www.apache.org/licenses/LICENSE-2.0
Accession Number:
edsbas.295C6CED
Database:
BASE

Further Information

SCALAR is a new platform for running real-time machine learning competitions on data streams. Following the intent of Kaggle, which serves as a platform for organizing machine learning competitions adapted for batch learning, we propose SCALAR as a novel platform explicitly designed for stream learning in real-time. SCALAR supports both classification and regression problems in the data streaming setting. It has been developed in Python, using state of the art open-source solutions: Apache Kafka, Apache Spark, gRPC, Protobuf, and Docker.