Using Knowledge Graphs to Detect Enterprise Architecture Smells

Muhamed Smajevic; Simon Hacks; Dominik Bork

doi:10.1007/978-3-030-91279-6_4

Using Knowledge Graphs to Detect Enterprise Architecture Smells

Muhamed Smajevic
, Simon Hacks
, Dominik Bork^*

^*Kontaktforfatter

Mærsk Mc-Kinney Møller Instituttet

TU Wien
KTH Royal Institute of Technology

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

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Abstract

Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.

Originalsprog	Engelsk
Titel	The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings
Redaktører	Estefanía Serral, Janis Stirna, Jolita Ralyté, Jānis Grabis
Forlag	Springer Science+Business Media
Publikationsdato	2021
Sider	48-63
ISBN (Trykt)	9783030912789
ISBN (Elektronisk)	978-3-030-91279-6
DOI	https://doi.org/10.1007/978-3-030-91279-6_4
Status	Udgivet - 2021
Begivenhed	14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2021 - Riga, Letland Varighed: 24. nov. 2021 → 26. nov. 2021

Konference

Konference	14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2021
Land/Område	Letland
By	Riga
Periode	24/11/2021 → 26/11/2021

Navn	Lecture Notes in Business Information Processing
Vol/bind	432 LNBIP
ISSN	1865-1348

Bibliografisk note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Dokumenter og links

10.1007/978-3-030-91279-6_4

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Citationsformater

Smajevic, Muhamed ; Hacks, Simon ; Bork, Dominik. / Using Knowledge Graphs to Detect Enterprise Architecture Smells. The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings. red. / Estefanía Serral ; Janis Stirna ; Jolita Ralyté ; Jānis Grabis. Springer Science+Business Media, 2021. s. 48-63 (Lecture Notes in Business Information Processing, Bind 432 LNBIP).

@inproceedings{fd99ad20f0884ba6afe3fab5ef37ce1c,

title = "Using Knowledge Graphs to Detect Enterprise Architecture Smells",

abstract = "Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.",

keywords = "Analysis, ArchiMate, Enterprise architecture, Knowledge graph, Model transformation",

author = "Muhamed Smajevic and Simon Hacks and Dominik Bork",

note = "Publisher Copyright: {\textcopyright} 2021, IFIP International Federation for Information Processing.; 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2021 ; Conference date: 24-11-2021 Through 26-11-2021",

year = "2021",

doi = "10.1007/978-3-030-91279-6\_4",

language = "English",

isbn = "9783030912789",

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publisher = "Springer Science+Business Media",

pages = "48--63",

editor = "Estefan{\'i}a Serral and Janis Stirna and Jolita Ralyt{\'e} and Jānis Grabis",

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Smajevic, M, Hacks, S & Bork, D 2021, Using Knowledge Graphs to Detect Enterprise Architecture Smells. i E Serral, J Stirna, J Ralyté & J Grabis (red), The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings. Springer Science+Business Media, Lecture Notes in Business Information Processing, bind 432 LNBIP, s. 48-63, 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2021, Riga, Letland, 24/11/2021. https://doi.org/10.1007/978-3-030-91279-6_4

Using Knowledge Graphs to Detect Enterprise Architecture Smells. / Smajevic, Muhamed; Hacks, Simon; Bork, Dominik.
The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings. red. / Estefanía Serral; Janis Stirna; Jolita Ralyté; Jānis Grabis. Springer Science+Business Media, 2021. s. 48-63 (Lecture Notes in Business Information Processing, Bind 432 LNBIP).

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - Using Knowledge Graphs to Detect Enterprise Architecture Smells

AU - Smajevic, Muhamed

AU - Hacks, Simon

AU - Bork, Dominik

PY - 2021

Y1 - 2021

N2 - Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.

AB - Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.

KW - Analysis

KW - ArchiMate

KW - Enterprise architecture

KW - Knowledge graph

KW - Model transformation

U2 - 10.1007/978-3-030-91279-6_4

DO - 10.1007/978-3-030-91279-6_4

M3 - Article in proceedings

AN - SCOPUS:85119831729

SN - 9783030912789

T3 - Lecture Notes in Business Information Processing

SP - 48

EP - 63

BT - The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings

A2 - Serral, Estefanía

A2 - Stirna, Janis

A2 - Ralyté, Jolita

A2 - Grabis, Jānis

PB - Springer Science+Business Media

T2 - 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2021

Y2 - 24 November 2021 through 26 November 2021

ER -

Smajevic M, Hacks S, Bork D. Using Knowledge Graphs to Detect Enterprise Architecture Smells. I Serral E, Stirna J, Ralyté J, Grabis J, red., The Practice of Enterprise Modeling - 14th IFIP WG 8.1 Working Conference, PoEM 2021, Proceedings. Springer Science+Business Media. 2021. s. 48-63. (Lecture Notes in Business Information Processing, Bind 432 LNBIP). doi: 10.1007/978-3-030-91279-6_4

Using Knowledge Graphs to Detect Enterprise Architecture Smells

Abstract

Konference

Bibliografisk note

Dokumenter og links

SDU link

Fingeraftryk

Citationsformater