The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction.

Nicola Ferro; Norbert Fuhr; Gregory Grefenstette; Joseph A. Konstan; Pablo Castells; Elizabeth M. Daly; Thierry Declerck; Michael D. Ekstrand; Werner Geyer; Julio Gonzalo; Tsvi Kuflik; Krister Lindén; Bernardo Magnini; Jian-Yun Nie; Raffaele Perego; Bracha Shapira; Ian Soboroff; Nava Tintarev; Karin Verspoor; Martijn C. Willemsen; Justin Zobel; Krister Lindn

doi:10.1145/3274784.3274789

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction.

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. WillemsenJustin Zobel, Krister Lindn

Ben-Gurion University of the Negev, University of Haifa

Research output: Contribution to journal › Article › peer-review

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

Original language	American English
Article number	1
Pages (from-to)	91-101
Number of pages	11
Journal	ACM SIGIR Forum
Volume	52
Issue number	1
DOIs	https://doi.org/10.1145/3274784.3274789
State	Published - 31 Aug 2018

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https://dblp.org/db/journals/sigir/sigir52.html#FerroFGKCDDEGGK18

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Ferro, N., Fuhr, N., Grefenstette, G., Konstan, J. A., Castells, P., Daly, E. M., Declerck, T., Ekstrand, M. D., Geyer, W., Gonzalo, J., Kuflik, T., Lindén, K., Magnini, B., Nie, J.-Y., Perego, R., Shapira, B., Soboroff, I., Tintarev, N., Verspoor, K., ... Lindn, K. (2018). The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. ACM SIGIR Forum, 52(1), 91-101. Article 1. https://doi.org/10.1145/3274784.3274789

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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 Werner Geyer and Julio Gonzalo and Tsvi Kuflik and Krister Lind{\'e}n and Bernardo Magnini and Jian-Yun Nie and Raffaele Perego and Bracha Shapira and Ian Soboroff and Nava Tintarev and Karin Verspoor and Willemsen, {Martijn C.} and Justin Zobel and Krister Lindn",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2018",

month = aug,

day = "31",

doi = "10.1145/3274784.3274789",

language = "American English",

volume = "52",

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Ferro, N, Fuhr, N, Grefenstette, G, Konstan, JA, Castells, P, Daly, EM, Declerck, T, Ekstrand, MD, Geyer, W, Gonzalo, J, Kuflik, T, Lindén, K, Magnini, B, Nie, J-Y, Perego, R, Shapira, B, Soboroff, I, Tintarev, N, Verspoor, K, Willemsen, MC, Zobel, J & Lindn, K 2018, 'The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction.', ACM SIGIR Forum, vol. 52, no. 1, 1, pp. 91-101. https://doi.org/10.1145/3274784.3274789

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 - Geyer, Werner

AU - Gonzalo, Julio

AU - Kuflik, Tsvi

AU - Lindén, Krister

AU - Magnini, Bernardo

AU - Nie, Jian-Yun

AU - Perego, Raffaele

AU - Shapira, Bracha

AU - Soboroff, Ian

AU - Tintarev, Nava

AU - Verspoor, Karin

AU - Willemsen, Martijn C.

AU - Zobel, Justin

AU - Lindn, Krister

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2018/8/31

Y1 - 2018/8/31

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

SN - 0163-5840

VL - 52

SP - 91

EP - 101

JO - ACM SIGIR Forum

JF - ACM SIGIR Forum

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M1 - 1

ER -

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction.

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