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

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

10 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Editörler	Jian Pei, Jie Tang, Fabrizio Silvestri
Yayınlayan	Association for Computing Machinery, Inc
Sayfalar	1244-1247
Sayfa sayısı	4
ISBN (Elektronik)	9781450338547
DOI'lar	https://doi.org/10.1145/2808797.2809374
Yayın durumu	Yayınlandı - 25 Ağu 2015
Etkinlik	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Süre: 25 Ağu 2015 → 28 Ağu 2015

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Ülke/Bölge	France
Şehir	Paris
Periyot	25/08/15 → 28/08/15

Bibliyografik not

Publisher Copyright:
© 2015 ACM.

Belgeye Erişim

10.1145/2808797.2809374

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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. editör / 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).

@inproceedings{e1606ec49ce84f729c594632441d18b2,

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.",

keywords = "Data streams, MOA, Spark MLlib, Spark streaming, Stream mining, Streaming linear regression",

author = "Bari{\c s} Akg{\"u}n and {\"O}ǧ{\"u}d{\"u}c{\"u}, {{\c S}ule G{\"u}nd{\"u}z}",

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",

year = "2015",

month = aug,

day = "25",

doi = "10.1145/2808797.2809374",

language = "English",

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

publisher = "Association for Computing Machinery, Inc",

pages = "1244--1247",

editor = "Jian Pei and Jie Tang and Fabrizio Silvestri",

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

}

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).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Streaming linear regression on spark MLlib and MOA

AU - Akgün, Bariş

AU - Öǧüdücü, Şule Gündüz

PY - 2015/8/25

Y1 - 2015/8/25

N2 - 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.

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.

KW - Data streams

KW - MOA

KW - Spark MLlib

KW - Spark streaming

KW - Stream mining

KW - Streaming linear regression

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

A2 - Pei, Jian

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ER -

Akgün B, Öǧüdücü ŞG. Streaming linear regression on spark MLlib and MOA. In Pei J, Tang J, Silvestri F, editörler, 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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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

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