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The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. / Ferro, Nicola; Fuhr, Norbert; Grefenstette, Gregory; Konstan, Joseph A.; Castells, Pablo; Daly, Elizabeth M.; Declerck, Thierry; Ekstrand, Michael D.; Tintarev, Nava; More Authors.

In: ACM SIGIR Forum, Vol. 52, No. 1, 2018, p. 91-101.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Ferro, N, Fuhr, N, Grefenstette, G, Konstan, JA, Castells, P, Daly, EM, Declerck, T, Ekstrand, MD, Tintarev, N & More Authors 2018, 'The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction' ACM SIGIR Forum, vol. 52, no. 1, pp. 91-101. https://doi.org/10.1145/3274784.3274789

APA

Ferro, N., Fuhr, N., Grefenstette, G., Konstan, J. A., Castells, P., Daly, E. M., ... More Authors (2018). The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. ACM SIGIR Forum, 52(1), 91-101. https://doi.org/10.1145/3274784.3274789

Vancouver

Ferro N, Fuhr N, Grefenstette G, Konstan JA, Castells P, Daly EM et al. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. ACM SIGIR Forum. 2018;52(1):91-101. https://doi.org/10.1145/3274784.3274789

Author

Ferro, Nicola ; Fuhr, Norbert ; Grefenstette, Gregory ; Konstan, Joseph A. ; Castells, Pablo ; Daly, Elizabeth M. ; Declerck, Thierry ; Ekstrand, Michael D. ; Tintarev, Nava ; More Authors. / The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. In: ACM SIGIR Forum. 2018 ; Vol. 52, No. 1. pp. 91-101.

BibTeX

@article{50964ba507294a7dbd06346eb9a444cc,
title = "The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction",
abstract = "This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.",
author = "Nicola Ferro and Norbert Fuhr and Gregory Grefenstette and Konstan, {Joseph A.} and Pablo Castells and Daly, {Elizabeth M.} and Thierry Declerck and Ekstrand, {Michael D.} and Nava Tintarev and {More Authors}",
note = "Accepted author manuscript",
year = "2018",
doi = "10.1145/3274784.3274789",
language = "English",
volume = "52",
pages = "91--101",
journal = "ACM SIGIR Forum",
issn = "0163-5840",
publisher = "Association for Computing Machinery (ACM)",
number = "1",

}

RIS

TY - JOUR

T1 - The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

AU - Ferro, Nicola

AU - Fuhr, Norbert

AU - Grefenstette, Gregory

AU - Konstan, Joseph A.

AU - Castells, Pablo

AU - Daly, Elizabeth M.

AU - Declerck, Thierry

AU - Ekstrand, Michael D.

AU - Tintarev, Nava

AU - More Authors, null

N1 - Accepted author manuscript

PY - 2018

Y1 - 2018

N2 - This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.

AB - This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.

U2 - 10.1145/3274784.3274789

DO - 10.1145/3274784.3274789

M3 - Article

VL - 52

SP - 91

EP - 101

JO - ACM SIGIR Forum

T2 - ACM SIGIR Forum

JF - ACM SIGIR Forum

SN - 0163-5840

IS - 1

ER -

ID: 45183054