Understanding Enterprise Architecture with Topic Modeling
Preliminary Research based on Journal Articles
Marco Nardello
1,*
, Charles Møller
1
and John Gøtze
2,3
1
Department of Materials and Production, Aalborg University, Aalborg, Denmark
2
IT University of Copenhagen, Copenhagen, Denmark
3
QualiWare aps, Farum, Denmark
Keywords: Enterprise Architecture, Topic Modelling, Content Analysis, Research Theme, Future Research, Machine
Learning, Latent Dirichlet Allocation.
Abstract: The next 3 years will be more important than the last 50 due to the digital transformation across industries.
Enterprise Architecture (EA), the discipline that should lead enterprise responses to disruptive forces, is far
from ready to drive the next wave of change. The state of the art in the discipline is not clear and the
understanding among researchers and practitioners is not aligned. To address these problems, we developed
a topic model to help structure the field and enable EA to evolve coherently. In this preliminary study, we
present the 360 identified topics in EA literature and their evolution over time. Our study supports and
combines the findings from previous research and provides both a deeper analysis and more detailed findings.
1 INTRODUCTION
Enterprise Architecture (EA) leads enterprise
responses to disruptive forces (Gartner Inc. 2017) and
industries are now on the brink of digital
transformation. Globally 72 percent of CEOs believes
that “the next 3 years will be more critical for their
industry than the last 50 years” (KPMG 2016, p.8).
KPMG also explained that “the speed of change will
be exponential” and 77 percent of CEOs “are
concerned about whether their organization is
keeping up with new technologies” (KPMG 2016,
p.7). However, the current state of the art of EA is not
clear, and its fundamental concepts are not shared
among researchers and practitioners (Saint-Louis &
Lapalme 2016; Rahimi et al. 2017). In addition, from
informal interviews the authors acknowledged that
EA researchers and practitioners find problematic to
position themselves in the field because they are
lacking its overview.
Two studies tried to address this problem
systematically (Simon et al. 2013; Saint-Louis &
Lapalme 2016) though their limited scope left
important research themes uncovered. Simon et al.
(2013) conducted a content analysis of EA including
articles published until 2010. Since then EA literature
has almost tripled (Saint-Louis 2016). A more recent
example, the Systematic Mapping Study (SMS) of
Saint-Louis and Lapalme (2016) focused on literature
from selected journals therefore excluding most
literature on EA. In addition, based on Debortoli’s et
al. (2016) article both studies are subject to human
bias. In the first, the authors conceptualized EA in a
model and classified literature applying it (Debortoli
et al. 2016). In the second one, the authors manually
identified topics.
These studies exclusively identified the topics
without specifying the concepts related to them and
their evolution over time. To date there is no study
that identifies topics in EA without major human
subjectivity bias. In addition, directions for future
research are mainly based on personal experience
(Korhonen et al. 2016; Lapalme et al. 2016).
Another problem, not restricted to EA, is the lack of
a structure of research fields and how they evolved
over time.
To identify EA’s topics and their evolution over
time we developed a Topic Model applying Latent
Dirichlet Allocation (LDA) method by Blei et al.
(2003). This method develops a list of topics, for each
one presents the most frequently occurring words and
identifies the most relevant literature (Blei et al.
2003). The LDA method has been applied
successfully in other fields like in Information
Systems research (Chen & Zhao 2015). In this
preliminary study, we investigate the following
640
Nardello, M., ller, C. and Gøtze, J.
Understanding Enter prise Architecture with Topic Modeling.
DOI: 10.5220/0006693106400648
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 640-648
ISBN: 978-989-758-298-1
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
research questions:
1. What are the topics in EA literature?
2. How did topics in EA literature evolve over
time?
We present a topic model of EA based on 602 journal
articles. An article following this preliminary study
will present a topic model including also non-journal
publications.
This study aims to guide coherently the
development of EA research and it mainly contributes
to the EA community in two ways. First, it allows the
community to identify topics in EA that are relevant
for them, opening new research opportunities.
Second, it identifies the literature related to each
topic.
A final contribution to research in general is to
present a new approach for structuring research fields
through the development of a topic model. This type
of efforts can support systematic literature reviews by
structuring the field and providing the context and
literature for this type of research.
The article continues presenting the background
literature in section 2, the method applied in section 3
and the key findings of this study in section 4. We
discuss the results in section 5 and the conclusions
and future research are presented in section 6.
2 BACKGROUND
The foundations of EA date back to the 1960s when
IBM initiated the Business Systems Planning (BSP)
methodology (Kotusev 2016). BSP was “a structured
approach to assist a business in establishing an
information systems plan to satisfy its near- and long-
term information needs” (IBM 1978). In the 1980s,
EA emerged from BSP (Kotusev 2016). Zachman
(1987) developed a framework for information
systems and Spewak and Hill (1993) defined
Enterprise Architecture Planning. EA Planning was
“the process of defining architectures for the use of
information in support of the business and the plan for
implementing those architectures” (Spewak & Hill
1993). Finally at the beginning of the new
millennium, The Open Group Architecture
Framework (Group 2009) developed a new EA
reference architecture and methodology that are today
widely used in practice (Simon et al. 2013).
Two articles mapped EA contributions (Simon et
al. 2013; Saint-Louis & Lapalme 2016). The first one
is a combined bibliometric study and content analysis
of EA research from Simon et al. (2013). In their
study, the top down content analysis classified EA
publications in a predefined scheme. In this way, the
authors biased the results of the content analysis by
defining the classification scheme based on their
understanding prior the study (Debortoli et al. 2016,
pp.112–113). In addition, analysing Saint-Louis' and
Lapalme's EA article distribution by year (2013, p.76)
is possible to infer that Simon et al. (2013) study did
not consider almost two thirds of EA literature
currently available. This is also supported by a search
on Scopus database for articles with "Enterprise
Architecture" in title, abstract and keywords. Around
1300 articles were published before 2010 and almost
2000 were published after.
The study of Saint-Louis and Lapalme (2016)
reviewed 171 journal articles. They presented a
bibliometric study and a summary of ten
conceptualizations of EA, namely framework, model,
discipline, integration, measurement, strategy,
principles, design, literature, and practitioner. One
limitation of their study is to have included only
journal articles from few sources. Other sources were
not considered. In addition, when the authors
classified EA topics they assigned only one topic per
article. This is restrictive because it is common for a
research article to include multiple topics (e.g., an
article about EA models for business alignment
covers both the EA model topic and business
alignment topic) (Chen & Zhao 2015, p.2).
Another challenge in EA research is that efforts
are weakly integrated (Simon et al. 2013, p.19). This
is supported by the fact that systematic studies in EA
are very limited in scope. Excluding the SMS
previously mentioned, the other three available in the
field focus respectively on enterprise integration
(Banaeianjahromi & Smolander 2016), applications
of ontologies (Pinto et al. 2014), measurement and
indicators (Abdallah et al. 2016). Among the
systematic literature reviews, we found areas as the
automated analysis and documentation (Florez et al.
2016; Farwick et al. 2016; Hauder et al. 2012),
aspects of EA Management (Kotusev 2017; Rahimi
et al. 2017; Jugel et al. 2016; Huber et al. 2017;
Brosius 2016; Lange et al. 2016; Schneider et al.
2013; Wißotzki & Sonnenberger 2012) and a research
group involved in EA implementation (Rouhani et al.
2015; Nikpay, R. Ahmad, et al. 2017; Nikpay, R. B.
Ahmad, et al. 2017).
Our study aims at overcoming the limitations of
previous research, outline the topics in EA research,
and present their evolution over time.
Understanding Enterprise Architecture with Topic Modeling
641
3 METHODOLOGY
There are five main methods for performing text-
categorization (Debortoli et al. 2016) – bottom-up
and top-down manual coding, dictionaries, and
supervised and unsupervised machine learning. We
applied the latter that uses documents to inductively
discover categories and assigns documents to these
categories (Debortoli et al. 2016). We chose this
method because it “generates reproducible results that
are not subject to the human subjectivity bias”
(Debortoli et al. 2016).
An application of unsupervised machine learning
is probabilistic topic modelling. This approach is
based on the assumption that “words that occur in the
same contexts tend to have similar meanings” (e.g.
the co-occurring words “mozzarella”, “tomato”,
“basil”, “margherita”, “oven” all refer to the “pizza”
topic) (Turney & Pantel 2010, p.142). Three main
methods are used in Information System research
(Debortoli et al. 2016): Latent Semantic Analysis
(LSA), Latent Dirichlet Allocation (LDA) and
Leximancer. We applied LDA because previous
studies proved that LSA methods suffer from
interpretability issues (Debortoli et al. 2016) and
Leximancer algorithms are scarcely documented
(Debortoli et al. 2016).
LDA methods are grounded on the assumption that
documents are generated from a set of topics and that
each topic is characterized by a distribution over
words (Chen & Zhao 2015, p.2). For this reason, a
document can contain multiple topics. In this way, we
identified research topics in EA discipline.
In this study, for the data gathering and data
filtering steps we followed (Petersen et al. 2015), and
for the data preparation, application of text-mining
techniques, and evaluation of the topic model we
followed (Boyd-Graber et al. 2014; Debortoli et al.
2016). The first three steps have been performed
manually while the last two were automated.
3.1 Data Gathering
We included journal articles that contained the words
“Enterprise Architecture” in the title, abstract or list
of keywords. The search was performed in July 2017
on the following databases (in parenthesis the number
of articles retrieved): Scopus (539 articles),
ABI/INFORM (167 articles), Business Source
Premier of EBSCO (205 articles), Web of Science
(287 articles), Compendex (306 articles), INSPEC (0
articles), IEEE Xplore Digital Library (31 articles)
and AIS Electronic Library (61 articles). The search
generated 715 unique articles. Depending on the
database the search was restricted to journal articles
or to peer-reviewed articles. When possible, we
limited our search to articles written in English. We
decided to include only journal articles to focus this
preliminary study on what we consider the most
reliable outlet of contributions in the field.
3.2 Data Filtering
Each combination of title and abstract represented
what we will refer to as a document. The first author
performed this filtering alone since in case of a large
number of studies and many of them are clearly
identifiable noise the process may be conducted
individually (Petticrew & Roberts 2008). When in
doubt, he was inclusive. The following criteria were
applied based on (Petersen et al. 2015). Inclusion
criteria:
The study is published in a journal
The study relates to EA, as defined in the
Introduction section.
Exclusion criteria:
Conference or journal editorials
Studies presenting book reviews
Studies not written in English
Books and grey literature
The filtering process had two steps, see Figure 1.
First, we removed all the articles that were not journal
publications. Second, we removed articles not related
to EA based on title and abstract. We used the
resulting 602 articles as input for the topic model.
Figure 1. Filtering process.
3.3 Data Preparation
When preparing the documents for the analysis we
followed the guidelines from Boyd-Graber et al.
(2014). We started by removing punctuation
notations from the documents (except for the dash
that connects words closely related) and formatted the
text in small caps. We did not apply stemming since
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
642
it combines terms that may have different meanings.
Next, we used Blei’s and Lafferty’s Turtotopics to
identify multi-word expressions (Blei & Lafferty
2009). They are also known as n-gram and “business
IT alignment” is an example of 3-gram word that
captures a single concept in EA. We used for this
analysis only the abstracts with the following settings:
p-value 0.1 and the n-word appears at least 3 times in
the entire corpus. Since some phrases were repeated
in shorter forms, we considered only the shortest form
that preserved the meaning of the longer phrases (e.g.
we considered “enterprise architecture framework”
instead of “enterprise architecture framework eaf”).
We modified the words in the document to
distinguish them from 1-gram words (e.g.
“EnterpriseArchitectureFramework”).
Afterwards, we extended Stanford’s Topic
Modeling Toolkit list of English language stopwords
(Ramage & Rosen 2011) with common words
pertaining to EA literature (see the Appendix for the
full list). We used this list to exclude all the non-value
adding words from the analysis. Removing the n top
words is an alternative approach, thought the result
were worse than using the stopword list.
3.4 Text-mining
For the text-mining we used Stanford Topic Modeling
Toolbox (v0.4.0) (Ramage & Rosen 2011). We
computed the topic model with different number of
topics 10-fold ranging from 10 to 450. The LDA
method had the following parameters: max iteration
3000, topic smoothing 0.01, term smoothing 0.01. For
training the model to fit the documents we
experimented with the two main approaches – Gibbs
sampler and variational Bayes approximation and
decided to use the latter since it produced better
results. At 360 topics the results were most
understandable. We did the topic labelling manually.
3.5 Evaluation
To evaluate the number of topics, we calculated the
perplexity of held-out documents with different
number of topics from 10 to 450 in 10-fold.
“Perplexity is a standard performance measure of
different models in natural language processing; a
lower value of perplexity indicates a better model
performance” (Chen & Zhao 2015). As the number of
topic increases, the perplexity decreases until 360
topics and after that it stabilizes.
4 FINDINGS
In this study, we identified 360 topics in EA research,
the keywords related to each of them and their
evolution overtime. Due to space limitations, we will
present only the ten topics that covered most of EA
body of knowledge. For these topics, the five most
recurring words and the percentage of coverage of the
topic over the literature are available in the appendix.
The full list of the topics is available online
(QualiWare 2017).
First, the Business Process topic refers to business
processes (e.g. in the financial industry) and includes
concepts and information to support decision makers.
Second, the Framework topic relates to EA
frameworks that are adequate for integrating and
structuring information. They can be used to capture
the baseline structure of the organization as well as to
clarify and achieve predetermined outcomes.
Third, the Enterprise Resource Planning (ERP)
Model topic refers to models of companies ERP
software specifying its requirements, features and
functions. It is related to the Business Process topic
and to the Business Process Reengineering practice.
Fourth, the Management Activity topic presents
activities done by managers that include objectives,
opportunities, clients, necessary resources,
information, and information security. These
activities relate also to the concept of holistic
approach.
Fifth, the Design topic refers to the design of the
components and their interaction of Business
Processes, Systems, Information Technology (IT) in
order to support the analysis and the collection of
information.
Sixth, the Modelling Methodology topic refers to
methodologies and approaches for modelling
efficiently and systematically different facets of the
organization. Two main fields of application are the
healthcare sector and the Virtual Enterprise (VE).
Seventh, the Analysis Method topic includes the
concepts of techniques, estimations, accuracy,
uncertainty, and validation. This topic is scientifically
investigated mostly through case studies.
Eight, the Meta Model topic is the one that covers
the most body of knowledge. It related to meta-
models and the Analysis Method and Models topics.
Ninth, the Model topic relates to integrated models
that support the evolution of company’s systems.
Finally, the Service topic refers to services and
includes concepts like users, resources, infrastructure,
and information systems.
For each topic, we plotted its usage over time by
using Stanford Topic Modeling Toolbox feature
Understanding Enterprise Architecture with Topic Modeling
643
Figure 2. Topic distribution over time.
“slice”. This feature creates subsets of the results
associating it with one or more variables (e.g. year of
publication, source, authors, and so on). By splitting
the results for each year it is possible to understand
when each topic emerged and which have a growing,
stable or decreasing trend. Figure 2 presents the
evolution of the topics previously described (the
vertical axis represents the total number of words
associated with each topic for each year).
Four topics emerged in the last century. The first
topics that appeared in EA research are the
Framework, Modelling Methodology, Analysis
Method and Model. The Framework topic was
introduced in 1990 and has it started being
significantly researched from 2005 and it reached its
peak in 2014. Research on the Modelling
Methodology topic was popular around the year 2000
and since then has been less researched. The Analysis
Method topic emerged in 1998 and it had a constant
growth from 2008 until it reached its peak in 2013.
Since then its has been less researched. Research on
the Model topic started in 1990 and grew constantly
from 2004 until now.
The remaining six topics were researched from
the year 2000 onwards. Starting with the Business
Process topic, it grew constantly from the year 2000
until 2010 and since then it became less researched.
The ERP model topic emerged in 2001 and peaked in
2012 and 2013. Since then research on this topic
decreased significantly. The Management Activity
topic started to be more researched in 2004 and since
then had a fluctuating trend. The Design topic
emerged in the year 2000, it peaked in 2006 and since
then had a decreasing importance. The Meta Model
topic emerged in 2004 and peaked in 2010. Since then
it had a fluctuating trend. Finally, the Service topic
grew constantly from 2003 until 2008. Since then it
had decreasingly researched.
Topic models also provide a new way of
navigating literature. As an example of topic-based
literature navigation we list the three articles that are
contributing the most to the Modelling Methodology
topic. First, Glazner (2011) applied simulation
methods in EA. Second, Kim et al. (2006) developed
a systematic modelling approach for VEs. Third,
Nugraha et al. (2017) presented “a business
architecture modeling methodology to support the
integration of primary health care”.
5 DISCUSSION
In this section, we will present the implications or our
research and its limitations.
The three main implications of our study are to
provide a better understanding of EA topics, integrate
existing findings, identify new topics.
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
644
First, our study helps researchers and practitioners
understand the topics in EA. It does so in three ways,
identifying the concepts related to each topic, in
which fields it has been mostly researched, and the
literature related to it. For instance, the Modelling
Methodology topic refers to modelling various facets
of the organization and not exclusively the IT or
business aspects. In addition, the focus of research
has been on efficient and systematic approaches. Our
topic model helps researchers and practitioners
working with Modelling Methodologies to
acknowledge of these concepts and the focus of
research. Afterwards, the Modelling Methodology
topic helps researchers and practitioners understand
in which fields this topic is most applied, health care
sector and VE. Finally, our study identifies the
articles related to the topic.
Moving on to the comparison of EA topics with
previous studies, we will use the top ten topics to
illustrate how the topics identified in this study are
related to previous studies. The details of this analysis
are available in the appendix. Seven topics are
covered in the content analysis of Simon et al. (2013)
and six are related to the topics identified by Saint-
Louis and Lapalme (2016). The topics in common are
on a higher abstraction level than the others. The two
that are not mentioned in previous studies are the ERP
Model and Service topics. In this study, depending on
the abstraction level of the topic some of them are
inline and combine the findings of previous studies
while others present new, and usually more specific,
research topics.
This latter point combined with the topic-guided
literature navigation can support literature studies by
making available articles that might be left out from
a keyword search.
Our study has three main limitations. First, the
decision to include only journal articles might have
caused significant contributions to be left out from
this study. Having said so, this study is with Simon et
al. (2013) among the ones that included the widest
body of literature. Second, concerning the topics and
their labelling, the model has been evaluated only by
the authors. Even though both co-authors publish in
the field of EA, external evaluation would improve
the reliability of our results. A third limitation is to
have analysed only the abstracts of the journal articles
and not the full text. Having said so, this is a standard
approach in Topic Modelling (Chen & Zhao 2015).
6 CONCLUSION
In this study, is a first step towards a map of topics of
EA. This preliminary research identified topics in EA
research based on 602 journal articles. We presented
the ten topics that cover most literature in the field
and based on these we discussed the contribution of
topic modelling to EA research. We presented how
these topics improve the understanding of EA
literature, how they integrate and combine previous
findings and how they can be used to navigate EA
literature with a topic-guided approach. In addition,
we have discussed how topic models can support
systematic literature reviews in EA.
Future research will focus on extending the body
of literature analysed to include also non-journal
publications. In addition, different text mining
approaches will be investigated to enhance the value
of topic models in EA research. Interesting
contributions that might be applied and further
developed are the hierarchical topic modelling (Blei
et al. 2010), correlated topic modelling (Blei &
Lafferty 2007), automatic labelling technique (Lau et
al. 2011) and supervised machine learning.
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APPENDIX
Topic
(number &
label)
Coverage
of the
body of
literature
5 most common words (word instances per
topic)
Present in the
content
analysis of
Simon et at.
(2013)
Present in
topics of
Saint-Louis
& Lapalme
(2016)
2 Business
Process
0,60% process (56,63), basis (12,57), BusinessProcess
(8,37), financial (6,81), integrate (5,12)
Included in
the Business
Architecture
La
y
e
r
No
11 Framework 0,75% framework (117,89),
EnterpriseArchitectureFramework (8,12),
inte
g
rated
(
7,91
)
, structured
(
3,95
)
, ade
q
uate
(
2,99
)
No EA-
Framework
29 ERP Model 0,74% model (17,80), erp (12,69), develop (7,20),
companies (6,36), EnterpriseResourcePlanning
(6,17)
No No
52 Management 0,68% management (51,17), managers (14,90),
InformationSecurity (9,61), activities (8,79),
information (7,15)
EA
Management
Related to
EA-Principles
102 Design 0,64% design (31,78), level (10,63), BusinessProcesses
(10,82), systems (9,75), component (9,71)
Similar to the
Documentatio
n
p
hase
EA-Design
118 Modelling
Methodology
0,62% modeling (58,62), HealthCare (17,55),
methodology (13,13), ModelingApproaches (4,85),
rapidly (4,60)
Modelling
element of the
Methodology
Related to
EA-Principles
and EA-
Strategy
211 Analysis
Method
0,61% method (55,63), analysis (26,92), CaseStudy (5,66),
estimates (3,87), technique (3,44)
Analysis
phase
Related to
EA-Principles
and EA-
Strategy
227 Meta
Model
0,78% metamodel (20,41), analysis (13,99), models (8,53),
perform (7,31), metamodels (6,81)
Meta Model
element of the
Methodology
No
319 Model 0,76% model (128,19), integrated (5,95), evolution (3,14),
contributes
(
2,96
)
, s
y
stems
(
2,28
)
Layers EA-Model
355 Service 0,69% service (43,66), services (39,65), resources (7,23),
user
(
6,36
)
, infrastructure
(
4,86
)
No No
Stopword list: enterprise, architecture, set, advances, number, presents, difficult, research, better, using, study, significant,
important, conducted, paper, findings, contribute, approach, common, general, source, right, based, attempts, diverse, large,
way, ensure, proposed, presented, web, proposes, allow, events, main, require, core, related, created, area, implemented,
emerged, numerous, use, private, defines, cover, scope, provided, recent, years, various, elements, includes, specific, ea, aim,
respect, different, multiple, small, nowadays, line, focuses, defined, major, new, increasing, insights, high, growing, helps,
poor, issues, gap, just, existing, result, simple, make, approaches, extend, results, base, studies, key, used, step, article, matter,
order, meet, enterprises, does, case, available, enable, business, review, future, provides, importance, means, needs, time,
today's, field, initiatives, network, needed, need, group, called, open, uses, best, lack, organization's, e.g, useful, enterprise's,
actual, making, work, setting, test, known, fact, typically, quickly, intended, shown, force, including, shows, primary, active,
describes, fully, bring, increase, allows, enables, era, takes, offer, offers, presenting, calls, range, example, today, real, works,
propose, consider, previous, cope, requires, focus, novel, suggested, introduced, establish, formal, highly, terms, makes, gives,
joint, early, gain, viewed, development, help, contribution, question, questions, contributions, utilised, utilized, second, broad,
emphasis, papers, suggests, concluded, described, concludes, plays, discusses, huge, continues, play, far, easy, given, applied,
follow, ongoing, ii, manner, so-called, issue, ad, hand, iii, i, later, taken, exist, like, versus, old, run, lot, starts, box, vast, end,
short, comes, going, left, author, authors, et, performance, lies, little, seen, ways, whilst, and/or, is/it
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