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 Lindn; Bernardo Magnini; Jian-Yun Nie; Raffaele Perego; Bracha Shapira; Ian Soboroff; Nava Tintarev; Karin Verspoor; Martijn C. Willemsen; Justin Zobel

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 Lindn, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. WillemsenJustin Zobel

Department of Information Systems

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	English
Pages (from-to)	91-101
Number of pages	11
Journal	SIGIR Forum (ACM Special Interest Group on Information Retrieval)
Volume	52
Issue number	1
DOIs	https://doi.org/10.1145/3274784.3274789
State	Published - 2018

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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., Lindn, K., Magnini, B., Nie, J.-Y., Perego, R., Shapira, B., Soboroff, I., Tintarev, N., Verspoor, K., ... Zobel, J. (2018). The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 52(1), 91-101. 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 Lindn 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",

year = "2018",

doi = "10.1145/3274784.3274789",

language = "אנגלית",

volume = "52",

pages = "91--101",

journal = "SIGIR Forum (ACM Special Interest Group on Information Retrieval)",

issn = "0163-5840",

publisher = "Association for Computing Machinery (ACM)",

number = "1",

}

Ferro, N, Fuhr, N, Grefenstette, G, Konstan, JA, Castells, P, Daly, EM, Declerck, T, Ekstrand, MD, Geyer, W, Gonzalo, J, Kuflik, T, Lindn, K, Magnini, B, Nie, J-Y, Perego, R, Shapira, B, Soboroff, I, Tintarev, N, Verspoor, K, Willemsen, MC & Zobel, J 2018, 'The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction', SIGIR Forum (ACM Special Interest Group on Information Retrieval), vol. 52, no. 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 - Lindn, 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

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 - מאמר

SN - 0163-5840

VL - 52

SP - 91

EP - 101

JO - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

JF - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

IS - 1

ER -

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

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