Open Collaborative Data : using OSS principles to share data in SW engineering
Runeson, Per LU
(2019)
41st International Conference on Software Engineering (ICSE), 2019
- Abstract
- Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence... (More)
- Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/76828097-dd57-45b3-b085-472f0890eb23
- author
- Runeson, Per
LU
- organization
- publishing date
- 2019-08-22
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results : New Ideas and Emerging Research (ICSE-NIER) - New Ideas and Emerging Research (ICSE-NIER)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 41st International Conference on Software Engineering (ICSE), 2019
- conference location
- Montreal, Canada
- conference dates
- 2019-05-25 - 2019-05-31
- external identifiers
-
- scopus:85071427540
- ISBN
- 978-1-7281-1759-1
- 978-1-7281-1758-4
- DOI
- 10.1109/ICSE-NIER.2019.00015
- project
- Open Collaborative Data as an Innovation Platform for Machine Learning Applications
- language
- English
- LU publication?
- yes
- id
- 76828097-dd57-45b3-b085-472f0890eb23
- date added to LUP
- 2019-02-12 08:41:14
- date last changed
- 2024-06-11 04:08:03
@inproceedings{76828097-dd57-45b3-b085-472f0890eb23,
abstract = {{Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems.}},
author = {{Runeson, Per}},
booktitle = {{2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results : New Ideas and Emerging Research (ICSE-NIER)}},
isbn = {{978-1-7281-1759-1}},
language = {{eng}},
month = {{08}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Open Collaborative Data : using OSS principles to share data in SW engineering}},
url = {{https://lup.lub.lu.se/search/files/57878258/OpenCollaborativeDataPreprint.pdf}},
doi = {{10.1109/ICSE-NIER.2019.00015}},
year = {{2019}},
}