RStorm

Developing and testing streaming algorithms in R

Research output: Contribution to journalArticleScientificpeer-review

7 Downloads (Pure)

Abstract

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, the streaming word count, and examples of several statistical applications of RStorm.
Original languageEnglish
Pages (from-to)123-132
JournalThe R Journal
Volume6
Issue number1
Publication statusPublished - 2014

Cite this

@article{5613d0cdeba34bd9af9781624a52b80f,
title = "RStorm: Developing and testing streaming algorithms in R",
abstract = "Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, the streaming word count, and examples of several statistical applications of RStorm.",
author = "M.C. Kaptein",
year = "2014",
language = "English",
volume = "6",
pages = "123--132",
journal = "The R Journal",
issn = "2073-4859",
publisher = "R Foundation for Statistical Computing",
number = "1",

}

RStorm : Developing and testing streaming algorithms in R. / Kaptein, M.C.

In: The R Journal, Vol. 6, No. 1, 2014, p. 123-132.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - RStorm

T2 - Developing and testing streaming algorithms in R

AU - Kaptein, M.C.

PY - 2014

Y1 - 2014

N2 - Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, the streaming word count, and examples of several statistical applications of RStorm.

AB - Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This paper presents RStorm for the development and evaluation of streaming algorithms analogous to these production packages, but implemented fully in R. RStorm allows developers of streaming algorithms to quickly test, iterate, and evaluate various implementations of streaming algorithms. The paper provides both a canonical computer science example, the streaming word count, and examples of several statistical applications of RStorm.

M3 - Article

VL - 6

SP - 123

EP - 132

JO - The R Journal

JF - The R Journal

SN - 2073-4859

IS - 1

ER -