Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artifact Compatibility for Enabling Collaboration in the Artificial Intelligence Ecosystem
FHNW University of Applied Sciences and Arts Northwestern Switzerland, CHE. (Telecommunication Systems)ORCID iD: 0000-0001-9968-2440
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0001-7368-4448
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (Telecommunication Systems)ORCID iD: 0000-0003-4814-4428
2018 (English)In: Lecture Notes in Business Information Processing, Springer, 2018, Vol. 336, p. 56-71Conference paper, Published paper (Refereed)
Abstract [en]

Different types of software components and data have to be combined to solve an artificial intelligence challenge. An emerging marketplace for these components will allow for their exchange and distribution. To facilitate and boost the collaboration on the marketplace a solution for finding compatible artifacts is needed. We propose a concept to define compatibility on such a marketplace and suggest appropriate scenarios on how users can interact with it to support the different types of required compatibility. We also propose an initial architecture that derives from and implements the compatibility principles and makes the scenarios feasible. We matured our concept in focus group workshops and interviews with potential marketplace users from industry and academia. The results demonstrate the applicability of the concept in a real-world scenario.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 336, p. 56-71
Series
Lecture Notes in Business Information Processing, ISSN 18651348
Keywords [en]
compatibility, licensing, marketplace, artificial intelligence, machine learning, deep learning
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-16465DOI: 10.1007/978-3-030-04840-2_5ISBN: 9783030048396 OAI: oai:DiVA.org:bth-16465DiVA, id: diva2:1217749
Conference
9th International Conference on Software Business, ICSOB 2018; Tallinn; Estonia; 11 June 2018 through 12 June 2018
Available from: 2018-06-13 Created: 2018-06-13 Last updated: 2019-01-09Bibliographically approved

Open Access in DiVA

fulltext(471 kB)71 downloads
File information
File name FULLTEXT01.pdfFile size 471 kBChecksum SHA-512
3af71faecf484e94123b8ab986d2f3be0fa3c62dd665f83874884fe879fd53acbb52216d37e7f005cda4e3a8ec73da085403823de37e4461e146f1907e907544
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Maksimov, Yuliyan V.Fricker, SamuelTutschku, Kurt
By organisation
Department of Software EngineeringDepartment of Computer Science and Engineering
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 71 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 367 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf