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; Nava Tintarev; null More Authors

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, Nava Tintarev, More Authors

Web Information Systems

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

36 Downloads (Pure)

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	ACM SIGIR Forum
Volume	52
Issue number	1
DOIs	https://doi.org/10.1145/3274784.3274789
Publication status	Published - 2018

Bibliographical note

Accepted author manuscript

Access to Document

10.1145/3274784.3274789

45183054 - performance-prediction-irAccepted author manuscript, 224 KB

Cite this

@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",

}

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

SN - 0163-5840

VL - 52

SP - 91

EP - 101

JO - ACM SIGIR Forum

JF - ACM SIGIR Forum

IS - 1

ER -

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Abstract

Bibliographical note

Access to Document

Fingerprint

Cite this