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Tailored performance dashboards—an evaluation of the state of the art

CONTEXT: Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). OBJECTIVE: We analyze existing research on tailored dashboards and investigate different proposed approaches. METHODOLOGY...

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Detalles Bibliográficos
Autores principales: Kruglov, Artem, Strugar, Dragos, Succi, Giancarlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507486/
https://www.ncbi.nlm.nih.gov/pubmed/34712785
http://dx.doi.org/10.7717/peerj-cs.625
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author Kruglov, Artem
Strugar, Dragos
Succi, Giancarlo
author_facet Kruglov, Artem
Strugar, Dragos
Succi, Giancarlo
author_sort Kruglov, Artem
collection PubMed
description CONTEXT: Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). OBJECTIVE: We analyze existing research on tailored dashboards and investigate different proposed approaches. METHODOLOGY: We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. RESULTS: Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. IMPLICATIONS: Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners.
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spelling pubmed-85074862021-10-27 Tailored performance dashboards—an evaluation of the state of the art Kruglov, Artem Strugar, Dragos Succi, Giancarlo PeerJ Comput Sci Software Engineering CONTEXT: Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). OBJECTIVE: We analyze existing research on tailored dashboards and investigate different proposed approaches. METHODOLOGY: We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. RESULTS: Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. IMPLICATIONS: Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners. PeerJ Inc. 2021-10-05 /pmc/articles/PMC8507486/ /pubmed/34712785 http://dx.doi.org/10.7717/peerj-cs.625 Text en ©2021 Kruglov et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Software Engineering
Kruglov, Artem
Strugar, Dragos
Succi, Giancarlo
Tailored performance dashboards—an evaluation of the state of the art
title Tailored performance dashboards—an evaluation of the state of the art
title_full Tailored performance dashboards—an evaluation of the state of the art
title_fullStr Tailored performance dashboards—an evaluation of the state of the art
title_full_unstemmed Tailored performance dashboards—an evaluation of the state of the art
title_short Tailored performance dashboards—an evaluation of the state of the art
title_sort tailored performance dashboards—an evaluation of the state of the art
topic Software Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507486/
https://www.ncbi.nlm.nih.gov/pubmed/34712785
http://dx.doi.org/10.7717/peerj-cs.625
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