TY - GEN
T1 - Analyzing big data streams with apache SAMOA
AU - Kourtellis, Nicolas
AU - de Francisci Morales, Gianmarco
AU - Bifet, Albert
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Apache Apache samoa (Scalable Advanced Massive Online Analysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. Apache Apache samoa provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, Apache Samza, and Apache Apex. Apache Apache samoa is written in Java and is available at https://samoa.incubator.apache.org/ under the Apache Software License version 2.0.
AB - Apache Apache samoa (Scalable Advanced Massive Online Analysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. Apache Apache samoa provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, Apache Samza, and Apache Apex. Apache Apache samoa is written in Java and is available at https://samoa.incubator.apache.org/ under the Apache Software License version 2.0.
UR - https://www.scopus.com/pages/publications/85076708400
U2 - 10.1007/978-3-030-34407-8_3
DO - 10.1007/978-3-030-34407-8_3
M3 - Conference contribution
AN - SCOPUS:85076708400
SN - 9783030339067
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 67
BT - Behavioral Analytics in Social and Ubiquitous Environments - 6th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2015; 6th International Workshop on Modeling Social Media, MSM 2015; 7th International Workshop on Modeling Social Media, MSM 2016; Revised Selected Papers
A2 - Atzmueller, Martin
A2 - Chin, Alvin
A2 - Lemmerich, Florian
A2 - Trattner, Christoph
PB - Springer
T2 - 6th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2015, held in conjunction with the 6th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2015, 6th International Workshop on Modeling Social Media, MSM 2015, held in conjunction with the 24th International World Wide Web Conference, WWW 2015 and 7th International Workshop on Modeling Social Media, MSM 2016, held in conjunction with the 25th International World Wide Web Conference, WWW 2016
Y2 - 12 April 2016 through 12 April 2016
ER -