Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends

Mohan Timilsina; Samuele Buosi; Ping Song; Yang Yang; Rafiqul Haque; Edward Curry

doi:10.13025/21123

Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends

Mohan Timilsina
, Samuele Buosi
, Ping Song
, Yang Yang
, Rafiqul Haque
, Edward Curry

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

6 Citations (Scopus)

2 Downloads (Pure)

Abstract

Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository's strengths against its limitations.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4712-4721
Number of pages	10
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.13025/21123 https://doi.org/10.1109/bigdata59044.2023.10386933
Publication status	Published - 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

dataspace
foundation model
linked
machine learning

Access to Document

10.13025/21123Licence: CC BY
10.1109/bigdata59044.2023.10386933

Paper 3 draft Accepted author manuscript, 902 KBLicence: CC BY

Cite this

Timilsina, M., Buosi, S., Song, P., Yang, Y., Haque, R., & Curry, E. (2023). Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 4712-4721). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.13025/21123, https://doi.org/10.1109/bigdata59044.2023.10386933

Timilsina, Mohan ; Buosi, Samuele ; Song, Ping et al. / Enabling Dataspaces Using Foundation Models : Technical, Legal and Ethical Considerations and Future Trends. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 4712-4721 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

@inproceedings{fa1ac16b72b3494c8925e20c8fb4b829,

title = "Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends",

abstract = "Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository's strengths against its limitations.",

keywords = "dataspace, foundation model, linked, machine learning",

author = "Mohan Timilsina and Samuele Buosi and Ping Song and Yang Yang and Rafiqul Haque and Edward Curry",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.13025/21123",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4712--4721",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, \{Jerry Chun-Wei\} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

}

Timilsina, M, Buosi, S, Song, P, Yang, Y, Haque, R & Curry, E 2023, Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 4712-4721, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.13025/21123, https://doi.org/10.1109/bigdata59044.2023.10386933

Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends. / Timilsina, Mohan; Buosi, Samuele; Song, Ping et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4712-4721 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Enabling Dataspaces Using Foundation Models

T2 - 2023 IEEE International Conference on Big Data, BigData 2023

AU - Timilsina, Mohan

AU - Buosi, Samuele

AU - Song, Ping

AU - Yang, Yang

AU - Haque, Rafiqul

AU - Curry, Edward

PY - 2023

Y1 - 2023

N2 - Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository's strengths against its limitations.

AB - Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository's strengths against its limitations.

KW - dataspace

KW - foundation model

KW - linked

KW - machine learning

UR - http://hdl.handle.net/10379/18227

UR - https://www.scopus.com/pages/publications/85184977089

U2 - 10.13025/21123

DO - 10.13025/21123

M3 - Conference Publication

T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

SP - 4712

EP - 4721

BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

A2 - Palpanas, Themis

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

A2 - Slezak, Dominik

A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 December 2023 through 18 December 2023

ER -

Timilsina M, Buosi S, Song P, Yang Y, Haque R, Curry E. Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 4712-4721. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.13025/21123, 10.1109/bigdata59044.2023.10386933

Enabling Dataspaces Using Foundation Models: Technical, Legal and Ethical Considerations and Future Trends

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Keywords

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