Abstract

Architectural Knowledge (AK) has been an integral part of Software Architecture specification since its original inception, but it has not been explicitly managed until recently. It can be described as a computational structure composed of design decisions and rationales. Recent research emphasizes that availability must be complemented by an effective use of this information. We propose the use of Linked Data techniques to define and manage AK, thus achieving flexible storage and scalable search. Our approach suggests storing the network of decisions in RDF format to be retrieved efficiently by means of SPARQL queries. As a side effect, many different AK structures can be described in this way, which then becomes a general format to describe AK. Using this approach, this work analyses some significant features regarding AK of several Linked Data tools, in order to determine which ones are the best/worst for sharing and reusing AK as Linked Data.

Recommended Citation

Roda, C., Navarro, E., & Cuesta, C.E. (2014). A Comparative Analysis of Linked Data Tools to Support Architectural Knowledge. In V. Strahonja, N. Vrček., D. Plantak Vukovac, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Transforming Organisations and Society through Information Systems (ISD2014 Proceedings). Varaždin, Croatia: Faculty of Organization and Informatics. ISBN: 978-953-6071-43-2. http://aisel.aisnet.org/isd2014/proceedings/ISDevelopment/1.

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A Comparative Analysis of Linked Data Tools to Support Architectural Knowledge

Architectural Knowledge (AK) has been an integral part of Software Architecture specification since its original inception, but it has not been explicitly managed until recently. It can be described as a computational structure composed of design decisions and rationales. Recent research emphasizes that availability must be complemented by an effective use of this information. We propose the use of Linked Data techniques to define and manage AK, thus achieving flexible storage and scalable search. Our approach suggests storing the network of decisions in RDF format to be retrieved efficiently by means of SPARQL queries. As a side effect, many different AK structures can be described in this way, which then becomes a general format to describe AK. Using this approach, this work analyses some significant features regarding AK of several Linked Data tools, in order to determine which ones are the best/worst for sharing and reusing AK as Linked Data.