GHENT UNIVERSITY
FACULTY OF ECONOMICS AND BUSINESS
ADMINISTRATION
ACADEMIC YEAR 2013 – 2014
The development of an optimal visualisation for
enterprise architecture (CHOOSE)
INTERMEDIATE REPORT
Master dissertation in fulfilment of the requirements for the degree of
Master of Science in Applied Economics: Business Engineering
Sarah Boone
under the leadership of
Prof. Poels
i
Contents
1
Literature – read........................................................................................................................... 1
1.1
Overview ................................................................................................................................ 1
2.1
Summary ................................................................................................................................ 1
1.2.1
Enterprise Architecture .................................................................................................. 1
1.2.2
EA in SMEs ...................................................................................................................... 1
1.2.3
Construction of visual notations: The “Physics” of Notations ....................................... 3
2
Literature – to read ...................................................................................................................... 6
3
Research questions ...................................................................................................................... 6
4
Methodology ................................................................................................................................ 6
5
Planning ........................................................................................................................................ 8
6
References .................................................................................................................................... 9
6.1
References used in this report .............................................................................................. 9
6.2
Literature – read .................................................................................................................. 10
6.3
Literature – to read.............................................................................................................. 11
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1 Literature – read
1.1
Overview
An overview of the literature that has been read can be found on page 10.
2.1
Summary
1.2.1 Enterprise Architecture
Enterprise architecture is a domain that originates from the 1980s. This was the period in which
information systems received more and more attention. However, those systems increased in size
and complexity, which lead to a lack of overview. In 1987, Zachman published a paper in which he
explained this problem and emphasized the fact that there was a need for an architecture that
could integrate different components of a system. Therefore, he established a framework, which
he first called an information systems architectural framework. Later on, he called it an enterprise
architecture framework [1, 2].
Since then, a lot of different frameworks concerning EA have been established. The most
commonly discussed frameworks in literature are Zachman, TOGAF, FEA, E2AF, Gartner, DoDAF,
FEAF, MDA and ARIS. All frameworks have a slightly different approach towards EA, but often
incorporate the same basics. Most frameworks are based on four points of view, namely what,
how, who and why [2-4].
Currently, EA is used as a holistic approach to align different domains of a company, such as its
strategy, products and services, processes, employees, information technology (IT), technical
infrastructure, etc. Those domains and the relationships between them form the architecture of
the company [4]. Lankhorst defines EA as “a coherent whole of principles, methods, and models
that are used in the design and realisation of an enterprise’s organisational structure, business
processes, information systems, and infrastructure.” (Lankhorst 2009, p.3)
1.2.2 EA in SMEs
In a lot of SMEs, there is a lack of overview of the company. In many cases, the CEO is the only one
who has a clear overview of the business, because there is no mechanism that enables the transfer
1
of that knowledge towards the employees. As a result, it is very difficult for the CEO to discuss
strategic matters with other people in the company [5, 6]. To overcome this problem, it would be
very useful to implement EA. However, existing EA approaches are often complicated and ask for a
lot of effort and time. And let time precisely be something SMEs do not have. Therefore, a new
approach called CHOOSE has been established by Bernaert et al. [5, 7]. In their research, it is
emphasized that there are six criteria that have to be taken into account when implementing IS
techniques in SMEs [3, 5, 7-9]:
1. SMEs have to be able to work on strategic matters in a time efficient way.
2. People with limited IT skills should be able to utilize it.
3. There should be no experts needed to apply the approach.
4. The approach should make it possible to document how things are done in the company.
5. The CEO has to be involved.
6. The expected revenues have to be larger than the expected risks and costs.
The metamodel of CHOOSE is developed with the intention to make it comprehensive, yet simple.
It is based on the KAOS metamodel, which consists of five major angles of incidence: goal
viewpoint, agent viewpoint, operation viewpoint, object viewpoint and behaviour viewpoint. To
obtain a metamodel that really suits the needs of SMEs, some adaptions have been made. CHOOSE
now consists of four essential concepts, namely goal, actor, operation and object (Figure 1) [3].
Figure 1: CHOOSE essential metamodel (Bernaert et al., 2013)
2
1.2.3 Construction of visual notations: The “Physics” of Notations
In his paper The “Physics” of Notations, Daniel Moody provides a theory that can be used as a
scientific basis when visual notations are developed [10].
When visual notations are constructed or evaluated, software engineering (SE) researchers put a
lot of emphasis on the semantics while little attention is paid to visual syntax. However, the way a
model is presented has a great influence on the effectiveness of it [10].
Design goal
Before the design of a visual notation can get started, it is very important to have a clear design
goal. Common design goals are for example aesthetics, simplicity, expressiveness and naturalness.
However, these goals are vague and subjective. A more objective and scientific goal is cognitive
effectiveness. “Cognitive effectiveness is defined as the speed, ease and accuracy with which a
representation can be processed by the human mind.” (Moody 2009) [10, 11]
Speed, ease and accuracy can be empirically evaluated, which makes cognitive effectiveness a
suitable variable to determine whether a notation is good or not.
Design rationale
Besides the lack of design goal, current visualisations are also constructed with a shortage of
design rationale. It is often not clear why certain design decisions are made, because the reasons
why they are made are not properly documented. This makes the design process rather opaque
[10, 12]. Anyhow, a lot of design decisions are made arbitrarily, based on common sense. For
example, in the UML notation entity classes are represented by a rectangle. This is defined by
assertion [10].
The way visual notations communicate
When a diagram is made, the main purpose is to have a medium through which information can be
transferred. The theory of communication of Shannon and Weaver explains the process of
transferring information. First of all, the sender encodes the message, which results in a signal.
Next, the receiver decodes the signal into comprehensible information. This theory is also
3
applicable on visual notations. In that case, the diagram creator establishes a diagram after which
the diagram user looks at it in order to acquire the information [10].
In order to enable the diagram creator to establish a diagram that is effective, the notation
designer can influence eight visual variables (Figure 2). Those visual variables are defined by Bertin
in 1983 [13].
Figure 2: visual variables (Bertin, 1983)
Design principles
Moody has defined nine principles that enable designers to create cognitively effective visual
notations [10].
1. Semiotic clarity: every semantic construct should correspond to exactly one graphical symbol
There are four anomalies that have to be avoided:
Redundancy
One semantic construct can be represented by multiple different graphical symbols.
Overload
One graphical symbol needs to represent more than one semantic construct.
Excess
A graphical symbol is created while it does not represent any semantic construct.
Deficit
There is no graphical symbol provided for a certain semantic construct.
Table 1: anomalies that can occur when there is no 1:1 correspondence between semantic constructs and graphical symbols
2. Perceptual discriminability: it has to be possible to distinguish different symbols clearly
The discriminability of a notation is mainly influenced by the visual distance between symbols.
Visual distance can be defined as the number of visual variables on which symbols differ,
combined with the magnitude of the differences. In general, a greater visual distance between
symbols leads to a faster and more accurate recognition.
Shape is the most important factor in distinguishing symbols. Therefore, it should get a lot of
attention when designing a notation and it should be utilised as the primary visual variable.
4
Clearly discriminable symbols can be obtained by using redundant coding. This means that
multiple visual variables are used at the same time to enlarge the difference between symbols.
3. Semantic transparency: the representation of a construct should suggest its meaning
Different associations can be used:

Perceptual resemblance

Metaphors

Common logical properties

Cultural associations

Functional similarities

…
Semantic transparency does not only apply on concepts, but also on relationships.
Relationships can be presented in ways that enable spontaneous interpretation.
4. Complexity management: do not overload the human mind
Diagrammatic complexity is measured by the number of elements on a diagram. This
principle is less relevant for this master thesis.
5. Cognitive integration: make integration of information from other diagrams possible
This principle is less relevant for this master thesis.
6. Visual expressiveness: use the full range and capacities of visual variables
Visual expressiveness can be seen as the number of visual variables that are used in a
notation and the extent to which they are used.
Colour is a strong mechanism, because differences in colour are seen much faster than
differences in other variables. However, colour should not be used in a non-redundant way.
Otherwise, differences disappear when a diagram is for example printed from a black-andwhite printer.
7. Dual coding: text should complement graphics
Information can be transferred more effectively when it is represented in a diagram in which
pictures and words are combined. Therefore, text should be used as a supplement for
graphics. It is very appropriate to use text as a way of redundant coding. However, textual
coding should not be the basis on which the distinction between symbols has to be made.
5
8. Graphic economy: the number of different graphical symbols must be cognitively manageable
The human mind can only process diagrams effectively when there are not too many
graphical symbols.
Although this principle is important and has to be taken into account, there are no problems
expected here since the metamodel of CHOOSE already takes into account that the diagrams
have to be accessible.
9. Cognitive fit: different tasks and users ask for different visual dialects
Since CHOOSE is specifically developed for SMEs, only one audience has to be taken into
account. Therefore, this principle does not need as much attention as other ones.
2 Literature – to read
An overview of the literature that still has to be read is stated on page 11.
3 Research questions

Which visualisation has the highest cognitive effectiveness?

Which visualisation results in the best trade-off between accuracy on the one hand and
time and effort on the other hand?
4 Methodology
First of all, the current visualisation of CHOOSE will be evaluated based on Moody’s theory, in a
similar way as in [14, 15]. This will give an overview of the aspects that need to be improved.
Based on the results of the evaluation, a couple of new visualisations will be designed. In order
to be able to make those diagrams, an example based on the metamodel will have to be made
first. In that example, every concept and relationship will be presented at least once. A first
attempt to make an example is already made (Figure 3). However, it is clear that it needs some
revision.
6
Figure 3: first attempt to make an example based on the CHOOSE metamodel
The number of models will depend on the different combinations of visual variables that will
seem necessary to be made. However, to keep the test that will be executed in a later stage
actionable, the number of new models should not be too high. Therefore, it is estimated that
around 4 – 5 models will be established.
Once the models will be made, they will have to be tested in order to know which one results in
the highest cognitive effectiveness. Currently, the idea for the experimental design looks as
follows:
The dependent variable is cognitive effectiveness. This variable can be split up in three
measurable variables:

Accuracy

Time

Mental effort
When combining those three variables, a fourth measure can be created, namely efficiency [16].
7
The independent variables are the eight visual variables [10, 13]:
Visual variable
Shape
Colour
Texture
Orientation
Scale
Nominal
Nominal
Nominal
Nominal
Visual variable
Brightness
Size
Horizontal position
Vertical position
Scale
Ordinal
Interval
Interval
Interval
Table 2: visual variables
Since there are so many independent variables, it would be convenient to use a fractional
factorial design and more specifically a Latin square. However, when using this experimental
design, only main effects can be derived while effects coming from interactions between
independent variables cannot be seen. Therefore, it is not decided yet how the test will be
executed.
Anyhow, there is a lot of work to be done before the test can be established. Therefore,
focussing on the construction of the test will not be a priority during the next three months.
5 Planning
Month
May 2013
June 2013
July 2013
August 2013 (2 weeks)
September 2013 (1 week)
October 2013
November 2013
December 2013
January 2014
February 2014
March 2014
April 2014
May 2014
June 2014
Action
Finish reading literature
Exams
Holidays
Finish writing literature review
Establish example
Design the different models
Finish designing the different models
Establish test
Finish establishing test
Execute test
Analyse test results
Exams
Write thesis
Write thesis
Write thesis
Write thesis
DEADLINE: 20th of May 2014
Presentation
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6 References
6.1
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
References used in this report
Zachman, J.A., A framework for information systems architecture. IBM Systems Journal, 1987.
26(3): p. 276-292.
Sessions, R. Comparison of the Top Four Enterprise Architecture Methodologies. 2007.
Bernaert, M., et al., CHOOSE: Towards a Metamodel for Enterprise Architecture in Small and
Medium-Sized Enterprises. 2013, Ghent University, K.U. Leuven, University of Antwerp. p. 1-45.
In progress.
Lankhorst, M., Enterprise architecture at work : modelling, communication and analysis. 2 ed.
2009, New York: Springer. 352p.
Bernaert, M., et al., Enterprise architecture for small and medium-sized enterprises: a starting
point for bringing EA to SMEs, based on adoption models, in Information systems and small
and medium-sized enterprises (SMEs): state of art of IS research in SMEs. 2013, Springer:
Berlin, Germany.
Kamsties, E., K. Hörmann, and M. Schlich, Requirements engineering in small and medium
enterprises. Requirements Engineering, 1998. 3(2): p. 84-90.
Bernaert, M. and G. Poels. Enterprise Architecture for Small and Medium-Sized Enterprises. in
7TH SIKS Conference on Enterprise Information Systems (EIS- 2012). 2012. Niewegein, The
Netherlands.
Bernaert, M., De zoektocht naar know-how, know-why, know-what en know-who: architectuur
voor kleinere bedrijven in vier dimensies. INFORMATIE (AMSTERDAM), 2011. 53(9): p. 34-41.
Bernaert, M. and G. Poels. The quest for know-how, know-why, know-what and know-who:
using KAOS for enterprise modelling. in 6th International Workshop on BUSinness/IT ALignment
and Interoperability (BUSITAL 2011). 2011. London, UK: Springer.
Moody, D.L., The "Physics" of Notations: Towards a Scientific Basis for Constructing Visual
Notations in Software Engineering. IEEE Transactions on Software Engineering, 2009. 35(5): p.
756 - 778.
Larkin, J.H. and H.A. Simon, Why a Diagram is (Sometimes) Worth Ten Thousand Words.
Cognitive Science, 1987. 11(1): p. 65-100.
Jintae, L., Design rationale systems: understanding the issues. IEEE Expert, 1997. 12(3): p. 7885.
Bertin, J., Semiology of Graphics: Diagrams, Networks, Maps. 1983, Madison, Wisconsin, USA:
University of Wisconsin Press.
Moody, D. and J. Hillegersberg, Evaluating the Visual Syntax of UML: An Analysis of the
Cognitive Effectiveness of the UML Family of Diagrams, in Software Language Engineering.
2009, Springer Berlin Heidelberg. p. 16-34.
Moody, D., P. Heymans, and R. Matulevičius, Visual syntax does matter: improving the
cognitive effectiveness of the i* visual notation. Requirements Engineering, 2010. 15(2): p. 141175.
Huang, W., P. Eades, and S.-H. Hong, Measuring effectiveness of graph visualizations: A
cognitive load perspective. Information Visualization, 2009. 8(3): p. 139-152.
9
6.2
Literature – read
Bernaert, Maxime "De Zoektocht Naar Know-How, Know-Why, Know-What En Know-Who:
Architectuur Voor Kleinere Bedrijven in Vier Dimensies." INFORMATIE (AMSTERDAM) 53, no. 9
(2011): 34-41.
Bernaert, Maxime and Geert Poels. "The Quest for Know-How, Know-Why, Know-What and KnowWho: Using Kaos for Enterprise Modelling." In 6th International Workshop on BUSinness/IT
ALignment and Interoperability (BUSITAL 2011), edited by Oscar Pastor Camil Salinesi, 83, 2940. London, UK: Springer, 2011.
Bernaert, Maxime , Geert Poels, Monique Snoeck and Manu De Backer. "Enterprise Architecture
for Small and Medium-Sized Enterprises: A Starting Point for Bringing Ea to Smes, Based on
Adoption Models." In Information Systems and Small and Medium-Sized Enterprises (Smes):
State of Art of Is Research in Smes. Berlin, Germany: Springer, 2013.
Bernaert, Maxime and Geert Poels. "Enterprise Architecture for Small and Medium-Sized
Enterprises." In 7TH SIKS Conference on Enterprise Information Systems (EIS- 2012), 30p.
Niewegein, The Netherlands, 2012.
Bernaert, Maxime, Geert Poels, Monique Snoeck and Manu De Backer. "Choose: Towards a
Metamodel for Enterprise Architecture in Small and Medium-Sized Enterprises." 1-45. In
progress: Ghent University, K.U. Leuven, University of Antwerp., 2013.
European Commission. "Commission Recommendation of 6 May 2003 (2003/361/Ec): SME
Definition." Official Journal of the European Union L124/36, (2003): 6p.
Figl, Kathrin and Derntl Michael. "The Impact of Perceived Cognitive Effectiveness on Perceived
Usefulness of Visual Conceptual Modeling Languages." In Conceptual Modeling - Er 2011
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Henderson-Sellers, Brian, Graham Low and Cesar Gonzalez-Perez. "Semiotic Considerations for the
Design of an Agent-Oriented Modelling Language." In Enterprise, Business-Process and
Information Systems Modeling, edited by Ilia Bider, Terry Halpin, John Krogstie, Selmin Nurcan,
Erik Proper, Rainer Schmidt, Pnina Soffer and Stanisław Wrycza, 113, 422-434: Springer Berlin
Heidelberg, 2012.
Huang, Weidong , Peter Eades and Seok-Hee Hong. "Measuring Effectiveness of Graph
Visualizations: A Cognitive Load Perspective." Information Visualization 8, no. 3 (2009): 139152.
Jintae, Lee. "Design Rationale Systems: Understanding the Issues." IEEE Expert 12, no. 3 (1997): 7885.
Kai, Xu, C. Rooney, P. Passmore, Ham Dong-Han and P. H. Nguyen. "A User Study on Curved Edges
in Graph Visualization." Visualization and Computer Graphics, IEEE Transactions on 18, no. 12
(2012): 2449-2456.
Kamsties, Erik, Klaus Hörmann and Maud Schlich. "Requirements Engineering in Small and Medium
Enterprises." Requirements Engineering 3, no. 2 (1998): 84-90.
Lankhorst, Marc. Enterprise Architecture at Work : Modelling, Communication and Analysis. 2 ed.
New York: Springer, 2009.
Moody, D. L. "The Method Evaluation Model: A Theoretical Model for Validating Information
Systems Design Methods." ECIS 2003 Proceedings, (2003).
10
Moody, D. L. "What Makes a Good Diagram? Improving the Cognitive Effectiveness of Diagrams in
Is Development." In Advances in Information Systems Development, 2, 481-492: Springer US,
2006.
Moody, D. L. "The "Physics" of Notations: Towards a Scientific Basis for Constructing Visual
Notations in Software Engineering." IEEE Transactions on Software Engineering 35, no. 5
(2009): 756 - 778.
Moody, Daniel, Patrick Heymans and Raimundas Matulevičius. "Visual Syntax Does Matter:
Improving the Cognitive Effectiveness of the I* Visual Notation." Requirements Engineering 15,
no. 2 (2010): 141-175.
Moody, Daniel and Jos Hillegersberg. "Evaluating the Visual Syntax of Uml: An Analysis of the
Cognitive Effectiveness of the Uml Family of Diagrams." In Software Language Engineering,
5452, 16-34: Springer Berlin Heidelberg, 2009.
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Schrepfer, Matthias, Johannes Wolf, Jan Mendling and HajoA Reijers. "The Impact of Secondary
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Anne Persson and Janis Stirna, 39, 161-175: Springer Berlin Heidelberg, 2009.
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Zachman, J. A. "A Framework for Information Systems Architecture." IBM Systems Journal 26, no. 3
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6.3
Literature – to read
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Technology 8, no. 5 (1989): 369-385.
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The European Cognitive Science Conference 2003: Institute of Cognitive Science, Osnabrück,
Germany, September 10-13, 2003, 115: Psychology Press, 2003.
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Visualization Effectiveness." IEEE Transactions on Visualization and Computer Graphics 18, no.
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Larkin, Jill H. and Herbert A. Simon. "Why a Diagram Is (Sometimes) Worth Ten Thousand Words."
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Sung-Hee, Kim, Dong Zhihua, Xian Hanjun, B. Upatising and Yi Ji Soo. "Does an Eye Tracker Tell the
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