Streaming linear regression on spark MLlib and MOA

Bariş Akgün; Şule Gündüz Öǧüdücü

doi:10.1145/2808797.2809374

Streaming linear regression on spark MLlib and MOA

Bariş Akgün, Şule Gündüz Öǧüdücü

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

In recent years, analyzing data streams has attracted considerable attention in different fields of computer science. In this paper, two different frameworks, namely MOA and Spark MLlib, are examined for linear regression on streaming data. The focus is placed on determining how well the linear regression techniques implemented in the frameworks that could be used to model the data streams. We also examine the challenges of massive data streams and how MOA and Spark Streaming solve these kinds of challenges. As a result of the experiments, we see that although the usage of MOA is more easier than Spark MLlib, Spark MLlib linear regression performance on streaming data is better.

Original language	English
Title of host publication	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Editors	Jian Pei, Jie Tang, Fabrizio Silvestri
Publisher	Association for Computing Machinery, Inc
Pages	1244-1247
Number of pages	4
ISBN (Electronic)	9781450338547
DOIs	https://doi.org/10.1145/2808797.2809374
Publication status	Published - 25 Aug 2015
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: 25 Aug 2015 → 28 Aug 2015

Publication series

Name	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Conference

Conference	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/Territory	France
City	Paris
Period	25/08/15 → 28/08/15

Bibliographical note

Publisher Copyright:
© 2015 ACM.

Keywords

Data streams
MOA
Spark MLlib
Spark streaming
Stream mining
Streaming linear regression

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10.1145/2808797.2809374

Cite this

Akgün, B., & Öǧüdücü, Ş. G. (2015). Streaming linear regression on spark MLlib and MOA. In J. Pei, J. Tang, & F. Silvestri (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 1244-1247). (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2809374

Akgün, Bariş ; Öǧüdücü, Şule Gündüz. / Streaming linear regression on spark MLlib and MOA. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. editor / Jian Pei ; Jie Tang ; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. pp. 1244-1247 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

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title = "Streaming linear regression on spark MLlib and MOA",

abstract = "In recent years, analyzing data streams has attracted considerable attention in different fields of computer science. In this paper, two different frameworks, namely MOA and Spark MLlib, are examined for linear regression on streaming data. The focus is placed on determining how well the linear regression techniques implemented in the frameworks that could be used to model the data streams. We also examine the challenges of massive data streams and how MOA and Spark Streaming solve these kinds of challenges. As a result of the experiments, we see that although the usage of MOA is more easier than Spark MLlib, Spark MLlib linear regression performance on streaming data is better.",

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note = "Publisher Copyright: {\textcopyright} 2015 ACM.; IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

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doi = "10.1145/2808797.2809374",

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series = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

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Akgün, B & Öǧüdücü, ŞG 2015, Streaming linear regression on spark MLlib and MOA. in J Pei, J Tang & F Silvestri (eds), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Association for Computing Machinery, Inc, pp. 1244-1247, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 25/08/15. https://doi.org/10.1145/2808797.2809374

Streaming linear regression on spark MLlib and MOA. / Akgün, Bariş; Öǧüdücü, Şule Gündüz.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. ed. / Jian Pei; Jie Tang; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. p. 1244-1247 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Öǧüdücü, Şule Gündüz

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AB - In recent years, analyzing data streams has attracted considerable attention in different fields of computer science. In this paper, two different frameworks, namely MOA and Spark MLlib, are examined for linear regression on streaming data. The focus is placed on determining how well the linear regression techniques implemented in the frameworks that could be used to model the data streams. We also examine the challenges of massive data streams and how MOA and Spark Streaming solve these kinds of challenges. As a result of the experiments, we see that although the usage of MOA is more easier than Spark MLlib, Spark MLlib linear regression performance on streaming data is better.

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Akgün B, Öǧüdücü ŞG. Streaming linear regression on spark MLlib and MOA. In Pei J, Tang J, Silvestri F, editors, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 1244-1247. (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). doi: 10.1145/2808797.2809374

Streaming linear regression on spark MLlib and MOA

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Keywords

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