A Recommender Plug-in for Enterprise Architecture Models

Part of ISBN 9789897586484QC 20231123
IT has evolved over the decades, where its role and impact have transitioned from being a tactical tool to a more strategic one for driving business strategies to transform organizations. The right alignment between IT strategy and business has become a compelling factor for Chief Information Officers and Enterprise Architecture (EA) in practice is one of the approaches where this alignment can be achieved. Enterprise Modeling complements EA with models that are composed of enterprise components and relationships, that are stored in a repository. Over time, the repository grows which opens up research avenues to provide data intelligence. Recommender Systems is a field that can take different forms in the modeling domain and each form of recommendation can be enhanced with sophisticated models over time. Within this work, we focus on the latter problem by providing a recommender architecture framework eases the integration of different Recommender Systems. Thus, researchers can easily compare the performance of different recommender systems for EA models. The framework is developed as a distributed plugin for Archi, a widely used modeling tool to create EA models in the ArchiMate notation.

Hacks, Simon,1988-Stockholms universitet,Institutionen för data- och systemvetenskap,Stockholm University, Stockholm, Sweden(Swepub:su)siha0922 (författare)
Katsikeas, Sotirios,PhD StudentKTH,Nätverk och systemteknik(Swepub:kth)u1b2g8nt (författare)
KTHNätverk och systemteknik (creator_code:org_t)

Ingår i:Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2, ICEIS 2023: INSTICC, s. 474-4809789897586484

Av författaren/redakt...: Raavikanti, Sash ...; Hacks, Simon, 19 ...; Katsikeas, Sotir ...

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Systemvetenskap ...

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Datorsystem

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