Enterprise architecture intelligence

R.K.M. Veneberg; Maria Eugenia Iacob; Marten J. van Sinderen; L. Bodenstaff

doi:10.1109/EDOC.2014.14

Enterprise architecture intelligence

R.K.M. Veneberg, Maria Eugenia Iacob, Marten J. van Sinderen, L. Bodenstaff

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

11 Citations (Scopus)

Abstract

Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture objects), and little has been written on how to do this. Therefore, in this paper we aim to fill this gap and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result either in an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses), or in a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data, and of business analytics results). The method is illustrated by means of a case study. Also, a model to store enterprise architecture, operational data and time is presented on which new forms of analysis may be performed.

Original language	Undefined
Title of host publication	Eighteenth IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014
Editors	M.U. Reichert, S. Rinderle-Ma, G. Grossmann
Place of Publication	USA
Publisher	IEEE Computer Society
Pages	22-31
Number of pages	10
ISBN (Print)	978-1-4799-5470-4
DOIs	https://doi.org/10.1109/EDOC.2014.14
Publication status	Published - 2014
Event	18th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014 - Ulm, Germany Duration: 3 Sep 2014 → 5 Sep 2014 Conference number: 18

Publication series

Name
Publisher	IEEE Computer Society
ISSN (Print)	1541-7719

Conference

Conference	18th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014
Abbreviated title	EDOC
Country/Territory	Germany
City	Ulm
Period	3/09/14 → 5/09/14

Keywords

Operational data
SCS-Services
EWI-25047
Data warehouse
Quantitative analysis
Business intelligence
IR-91750
METIS-306021
Enterprise Architecture

Access to Document

10.1109/EDOC.2014.14

http://eprints.eemcs.utwente.nl/secure2/25047/01/5470a022.pdf

Cite this

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title = "Enterprise architecture intelligence",

abstract = "Combining enterprise architecture and operational data is complex (especially when considering the actual {\textquoteleft}matching{\textquoteright} of data with enterprise architecture objects), and little has been written on how to do this. Therefore, in this paper we aim to fill this gap and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result either in an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses), or in a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data, and of business analytics results). The method is illustrated by means of a case study. Also, a model to store enterprise architecture, operational data and time is presented on which new forms of analysis may be performed.",

keywords = "Operational data, SCS-Services, EWI-25047, Data warehouse, Quantitative analysis, Business intelligence, IR-91750, METIS-306021, Enterprise Architecture",

author = "R.K.M. Veneberg and Iacob, {Maria Eugenia} and {van Sinderen}, {Marten J.} and L. Bodenstaff",

note = "10.1109/EDOC.2014.14 ; null ; Conference date: 03-09-2014 Through 05-09-2014",

year = "2014",

doi = "10.1109/EDOC.2014.14",

language = "Undefined",

isbn = "978-1-4799-5470-4",

publisher = "IEEE Computer Society",

pages = "22--31",

editor = "M.U. Reichert and S. Rinderle-Ma and G. Grossmann",

booktitle = "Eighteenth IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014",

address = "United States",

}

Veneberg, RKM, Iacob, ME , van Sinderen, MJ & Bodenstaff, L 2014, Enterprise architecture intelligence. in MU Reichert, S Rinderle-Ma & G Grossmann (eds), Eighteenth IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014. IEEE Computer Society, USA, pp. 22-31, 18th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014, Ulm, Germany, 3/09/14. https://doi.org/10.1109/EDOC.2014.14

Enterprise architecture intelligence. / Veneberg, R.K.M.; Iacob, Maria Eugenia ; van Sinderen, Marten J. et al.

Eighteenth IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014. ed. / M.U. Reichert; S. Rinderle-Ma; G. Grossmann. USA : IEEE Computer Society, 2014. p. 22-31.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Veneberg, R.K.M.

AU - Iacob, Maria Eugenia

AU - van Sinderen, Marten J.

AU - Bodenstaff, L.

N1 - Conference code: 18

PY - 2014

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N2 - Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture objects), and little has been written on how to do this. Therefore, in this paper we aim to fill this gap and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result either in an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses), or in a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data, and of business analytics results). The method is illustrated by means of a case study. Also, a model to store enterprise architecture, operational data and time is presented on which new forms of analysis may be performed.

AB - Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture objects), and little has been written on how to do this. Therefore, in this paper we aim to fill this gap and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result either in an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses), or in a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data, and of business analytics results). The method is illustrated by means of a case study. Also, a model to store enterprise architecture, operational data and time is presented on which new forms of analysis may be performed.

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KW - Data warehouse

KW - Quantitative analysis

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KW - IR-91750

KW - METIS-306021

KW - Enterprise Architecture

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BT - Eighteenth IEEE International Enterprise Distributed Object Computing Conference, EDOC 2014

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