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From Anarchy to System: A Novel Classification of Visual Knowledge Codification Techniques: Novel Classification of Visual Knowledge Codification Techniques


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The paper suggests a classification of visual knowledge codification (diagramming) techniques for multi-perspective business systems analysis and design. The classification is based on seven main categories of knowledge: what-knowledge, how-knowledge, who-knowledge, why-knowledge, what for-knowledge, when-knowledge and where-knowledge. The classification defines both knowledge type and the most appropriate kind of diagramming technique. Examples for use of this classification system for marketing function applications are presented. Specific examples of these applications include mind map, concept map, Ishikawa diagram, strategy map, business process models and function trees. It is hoped that the new classification will allow better practical use of diagramming techniques in business and lead to fewer professional misunderstandings and more effective business communication. The aim of this research is thus to improve visual literacy among both business practitioners and educators.
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Research Article
From Anarchy to System: A Novel
Classication of Visual Knowledge
Codication Techniques
Dmitry Kudryavtsev*and Tatiana Gavrilova
Graduate School of Management, Saint-Petersburg State University, Russia
The paper suggests a classication of visual knowledge codication (diagramming) techniques for multi-perspective
business systems analysis and design. The classication is based on seven main categories of knowledge:
what-knowledge, how-knowledge, who-knowledge, why-knowledge, what for-knowledge, when-knowledge and
where-knowledge. The classication denes both knowledge type and the most appropriate kind of diagramming
technique. Examples for use of this classication system for marketing function applications are presented. Specic
examples of these applications include mind map, concept map, Ishikawa diagram, strategy map, business process
models and function trees. It is hoped that the new classication will allow better practical use of diagramming
techniques in business and lead to fewer professional misunderstandings and more effective business communication.
The aim of this research is thus to improve visual literacy among both business practitioners and educators. Copyright
© 2016 John Wiley & Sons, Ltd.
Knowledge visualization has proved to be an
effective tool for knowledge creation, acquisition
and transfer (Eisenstadt et al., 1990; Eppler and
Burkhard, 2007; Gavrilova and Gulyakina, 2011).
According to Eppler and Burkhard (2007), there
are several formats of visual knowledge representa-
tion: heuristic sketches (e.g. the ad hoc, joint draw-
ings of complex ideas in meetings), conceptual
diagrams (such as Ishikawa diagrams), visual
metaphors (such as an iceberg visualization
distinguishing implicit and explicit forms of knowl-
edge), knowledge maps (such as a landscape of
in-house experts) and interactive visualizations (to
help users to explore complex information in inter-
active form). These graphic formats capture not
only (descriptive) facts or numbers (information
visualization), but also contain prescriptive and
prognostic insights, principles, basic assumptions
and relations. The focus of the current research is
on qualitative conceptual diagrams schematic
depictions of abstract ideas that use standardized
shapes to structure information and illustrate rela-
tions. Conceptual diagrams help to make abstract
concepts accessible, reduce complexity to the key
issues (Huff, 1990), amplify cognition and facilitate
discussion of relationships. In this paper, we will
often use diagramming and visual knowledge
codication techniques synonymously.
Diagrams (Blackwell and Engelhardt, 2002)
constitute the basis for visual knowledge represen-
tation, and elaborate diagrammatic techniques
typically form visual modelling languages (Harel
and Rumpe, 2000). In computer science, these
techniques are reected in such languages as UML
(Rumbaugh et al., 2004) and IDEF (Mayer et al.,
1992). They are also integrated in software engineer-
ing methods, for example, the Structured Analysis
and Design Technique, and are organized by archi-
tecture frameworks, such as the Zachman frame-
work (Zachman, 2003).
The focus of this paper is the realm of manage-
ment. Managers frequently use diagrams in their
work (Galloway, 1994; Hodgkinson et al., 2004;
Lengler and Eppler, 2007), but the choice of dia-
grams is often error-prone and inconsistent (Eppler
and Jianxin, 2008).
For the effective choice of visualization method,
at least ve perspectives should be considered
(Eppler and Burkhard, 2007). These perspectives
*Correspondence to: Dr Dmitry Kudryavtsev, Graduate School of
Management, Saint-Petersburg State University, Russia.
Knowledge and Process Management
Volume 24 Number 1 pp 313 (2017)
Published online 20 May 2016 in Wiley Online Library
( DOI: 10.1002/kpm.1509
Copyright © 2016 John Wiley & Sons, Ltd.
answer ve key questions with regard to visualizing
knowledge, namely:
(i) What type of knowledge is visualized (content)?
(ii) Why should that knowledge be visualized
(purpose, knowledge management process)?
(iii) For whom is the knowledge visualized (target
(iv) In which context should it be visualized (commu-
nicative situation: participants, place/media)?
(v) How can the knowledge be represented
(method, format)?
The focus of this paper is knowledge type, and it
can be used for identifying the type of knowledge
with respect to its content. Any complex entity can
be viewed from several perspectives (aspects,
facets) and in different strata (layers) (Gavrilova
and Voinov, 1998; Kingston and Macintosh, 2000;
Zachman, 2003). The following differentiated
question-based aspects are proposed (Kingston
and Macintosh, 2000; Alavi and Leidner, 2001;
Zachman, 2003; Gavrilova and Gulyakina, 2011):
WHAT-Knowledge: conceptual representation
WHAT_FOR-Knowledge: strategic representation
HOW_TO-Knowledge: functional representation
WHO-Knowledge: organizational representation
WHERE-Knowledge: spatial representation
WHEN-Knowledge: temporal representation
WHY-Knowledge: causal representation.
Today, there is no validated prescriptive frame-
work that links business diagrams with knowledge
types and that offers specic diagrams for particular
knowledge types. This denes the research ques-
tion: What types of conceptual diagrams are most suit-
able for specic types of knowledge (content)?
Diagram classications
A periodic table of visualization methods (Lengler
and Eppler, 2007) provides good overview of dia-
grams for managers. These authors decided that
the classication dimensions should be easy to use
and have some proven benets. The organization
principles were related to the situation in which
the visualization is used (when?), the type of con-
tent that is represented (what?), the expected visual-
ization benets (why?) and the actual visualization
format used (how?). They organized these dimen-
sions in a specic table of visualization methods.
However, we conclude that while this is an impres-
sive result, the content (what?) dimension, for the
focus of the current research, is not sufciently spec-
ied and includes only process (stepwise cyclical in
time and/or continuous sequential) and structure
(hierarchy or network).
Lohse et al. (1994) reported a structural classica-
tion of visual representations, identifying 11 major
clusters: graphs, tables, graphical tables, time charts,
networks, structure diagrams, process diagrams,
maps, cartograms, icons and pictures. Criteria for
classication were represented using ten anchor-point
phrases: spatial-nonspatial, temporal-nontemporal,
hard to understand-easy to understand, concrete-
abstract, continuous-discrete, attractive-unattractive;
emphasizes whole-emphasizes parts, numeric-
nonnumeric, static structure-dynamic process and
conveys much information-conveys little informa-
tion. We conclude that this classication works
mostly in the structural dimension. The semantic
dimension of diagrams is not covered.
Systemic visual modelling frameworks
Multi-perspective modelling framework (Kingston
et al., 1997; Kingston and Macintosh, 2000) is close
to our research, suggesting analysing information
or knowledge from six perspectives (Who, What,
How, When, Where and Why) at up to six levels of
detail (ranging from scopingthe problem to an
implemented solution). The authors suggest that
knowledge engineers should apply whatever
modelling techniques they prefer, as long as all the
necessary perspectives are covered. They propose
some modelling techniques that are appropriate
for particular perspectives or levels of abstraction.
However, their description of perspectives can be
extended using diagnostic questions and elements
of conceptual models; furthermore, their recom-
mendations for diagramming techniques should be
more specic and business/management oriented.
Glassey (2008) suggested method and instruments
for visual modelling of integrated knowledge. This
approach provides conceptual diagrams for know-
how,know-what,know-whoand know-
whyknowledge types. Additionally, it supports
interlinks between knowledge types using matrices.
But this approach does not cover all the necessary
knowledge types (e.g. when-knowledge) and pro-
vides a narrow set of diagramming techniques with a
focus on UML (e.g. UML collaboration, sequence and
collaboration diagrams for how-knowledge).
Knowledge maps (knowledge about knowledge)
are also included in the Glassey visual modelling
kit, but we think that they form another layer with
all types of knowledge and should be considered
independently (Eppler, 2008). Glassey method and
instruments are close to enterprise modelling meth-
odologies, which also provide comprehensive visual
modelling languages such as Archimate (The Open
Group, 2012) and MEMO (Frank, 2002). However,
these methodologies do not include all the popular
types of diagram used by managers, are rigid
compared with the suggested approach and should
be considered as the next maturity level in
4 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
organizational visual literacy (this topic is also
addressed in the Limitations of the Approach section
and in the conclusion).
This paper is in line with design science research
a research paradigm in which a researcher answers
questions relevant to human problems via the
creation of innovative artefacts, thereby contribut-
ing new knowledge to the body of scientic
evidence (Hevner and Chatterjee, 2010). Artefact
design implies constructive research methods.
According to Peffers et al. (2008), the design science
research process comprises six steps: problem
identication and motivation; identication of the
objectives for a solution; design and development;
demonstration; evaluation; and communication.
(a) Problem identication and motivation were
provided in the Introduction.
(b) Identication of the objectives involved speci-
cation of the requirements for the innovative
artefact. R1: ability to choose the category or
specic type of diagram for every type of
knowledge; R2: ability to formulate the user s
need for the visual knowledge codication in
his/her own wordsas the customer voice,
following the logic of House of Quality (Hauser
and Clausing, 1988); R3: provision of a limited
set of popular and mature diagramming tech-
niques (no requirement for complete coverage).
(c) The design and development step has resulted
in the method for selection of diagraming tech-
niques based on a multi-perspective knowledge
typology. The suggested method is based on
semantic analysis of knowledge types, diagram-
ming techniques and mappings between them.
This analysis is based on the concept of
ontology (Gruber, 1995; Maedche et al., 2003;
Rodríguez and Egenhofer, 2003); more speci-
cally, we use ontology design patterns
(Gangemi and Presutti, 2009) in our approach.
For further detail, see (Kudryavtsev et al.,
2013). This work describes the theoretical basis
of the novel classication.
Several sources of diagramming techniques were
used in order to reect both empirical and theoreti-
cal aspects.
One of the authors has participated in manage-
ment consultancy projects since 2003, in the
Business Engineering Group company. He took part
in some 15 projects in the area of enterprise perfor-
mance management, organizational design, busi-
ness process management, business transformation
and IT strategy development. These projects were
in Russia and Ukraine; the clients were medium-
sized industrial companies. Such projects implied
extensive analysis of organizational documents
with different conceptual diagrams, such as analyti-
cal reports, business presentations, procedures and
corporate standards. This documentary secondary
data (Cassell and Symon, 2004; Saunders et al.,
2011) was one of the sources for identication of
diagramming techniques used in business.
Popular diagramming tools suggest predened
techniques (templates) and their classication.
Because the vendors of these tools have advanced
marketing knowledge of customer needs in diagram-
ming, their selection of techniques is representative
for the current situation. Visio 2010 (https://prod- provides the following
eight embedded categories: Business, Engineering,
Flowchart, General, Maps and oor plans, Network,
Schedule, Software and Database. Smart Draw
( provides the follow-
ing 30 categories, including Flowchart, Mind Map,
Organizational Chart, Cause & Effect Diagram, Deci-
sion Tree, Project Management, Education, Engineer-
ing, Learning and Strategic Planning.
In addition to the empirical sources of diagram-
ming techniques, we also reviewed the literature in
order to add some mature techniques to insuf-
ciently covered knowledge types, for example, the
argument-mapping technique (Kirschner et al.,
2003) was added to the WHY-knowledge category.
Given the vast number of different diagramming
techniques, we applied some criteria: (1) Qualitative
diagrams are included; (2) We consider modelling
business objects at a general systemic level, so we ex-
cluded domain-specic diagrams (e.g. Portersve
forces diagram includes business strategy-specic
concepts, such as Suppliers, Substitutes, Buyers
and New Entrants); (3) We excluded matrix-based
methods, such as Growth-Share Matrix and SIPOC
tables (Rasmusson, 2006) (see (Phaal et al., 2006)
and their matrix-tool catalogue for further detail).
(d) Demonstration. The paper demonstrates the
use of the method to solve practical problems,
so a case study was used (Hevner and
Chatterjee, 2010).
(e) Evaluation. Comparison of the suggested
method with the requirements is presented in
the next section.
We sugge s t s p ec ication of knowledge types (Figure 1
and Table 1) and classication of diagrams/visual
knowledge codication techniques (Figure 2) in order
to choose diagrams for the particular knowledge type.
Informal specication of knowledge types (Figure 1) is
done using the competency questions technique
(Gruninger and Fox, 1995; Ren et al., 2014). More
Novel Classication of Visual Knowledge Codication Techniques 5
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
formal specication describes concepts and relation-
ships (Stock, 2010), which are associated with the
The method for the choice of diagram is
represented using the following steps:
(i) Articulate the question (in your own words)
that corresponds to your knowledge codica-
tion needs (see R2 from requirements speci-
cation in the previous section);
(ii) Align your question with the competency
questions from Figure 1, to nd similar one(s).
This will help you to dene the relevant
knowledge type (see R1);
(iii) Check your choice of knowledge type using
Table 1. Align the key words and meaning of
your question with the concepts and relation-
ships which are associated with the knowledge
types (see R2);
(iv) Identify the diagrams which are associated
with the necessary knowledge type using
Figure 2 (see R3);
(v) Choose from the short-list the most relevant
diagram type, which is associated with the
necessary knowledge type.
The Repair Service Company (RSC) is a subsidiary
of one of the biggest oil reneries in Russia. It was
Figure 1 Description of knowledge types using competency questions. [Colour gure can be viewed at]
Table 1 The list of concepts and relationships for the knowledge types
WHAT-knowledge Key concepts: Entity, Concept, Class, Instance, Property;
Relationships: subClassOf, hasPart/isPartOf, type, classies/isClassiedBy;
HOW-knowledge Key concepts: Action, Task, Process, Project, Work package, Inputs/Outputs;
Relationships: sequence: precedes/follows; hasInput/hasOutput;
Supporting concepts: Gateways, Events, Resource, Inputs/Outputs
WHO-knowledge Key concepts: Agent, Role, Organizational Unit, Organizational Position, Person;
Relationships: playsRole/is Role, is Responsible for, executes/isExecutedBy OR
performs/isPerformedBy, participateIn/has participant, subordinateOf
WHY-knowledge Key concepts: Cause, Effect, Variable, Problem, Premise;
Relationships:inuence on/is inuence by, has cause, supports.
WHAT FOR-knowledge Key concepts: Goal, Objective, End, Mean, Requirement, Value;
Relationships: help achieve/is achieved by, conicts with, subGoalOf, inuence, has effect;
WHEN-knowledge Key concepts: Start/End time, Duration, Time interval, Time instant (hour, minute, second),
Date (Year, Month);
Relationships: isDurationOf, has Start/End date, has Start/End time, before/after.
WHERE-knowledge Key concepts: Region, Place, Location;
Relationships: located in/is location of
6 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
founded in the late 1990s by separating the
maintenance and repairs department from the
parent company. Nowadays, RSC is an independent
enterprise, despite its 100% ownership by the oil
renery, with which it continues to maintain strong
relationships. RSC rents space from the parent
organization and therefore is located in the area of
the renery. Its main business activities are repair
of dynamic (e.g. pumps) and electric equipment
together with manufacturing spare parts.
At the moment, the primary customer of RSC is
its parent organization (over 90% of revenue). In
turn, RSC is the only company that provides repair
services to the oil renery. In the long term, the
renery wants to see RSC as a strategic partner.
There are many issues currently faced by RSC, one
of the most important being widening its range of
customers (external market) and developing its
own distribution network. At the moment, RSC
sells its production and services directly; it has no
marketing department, as it has only just begun to
plan its entry into the external market.
In order to establish a marketing capability, RSC
must clarify what is marketing?by specifying
the main marketing concepts, dene what should
the company do in the marketing domain?,what
are the current problems and their causes in the
marketing domain?,what are the objectives and
performance metrics of the marketing domain?,
how should RSC organize marketing processes?,
who will be responsible for these marketing
processes?and nally, how should it establish
marketing capability?.
In order to answer all these questions, the
company used both diagrams and standard formats
including text, for example, to describe principles in
the marketing domain; and tables, for example, to
describe business process suppliers, inputs, outputs
and customers (SIPOC tables: Rasmusson, 2006);
roles and responsibilities (RACI matrix: Smith and
Erwin, 2005); and Balanced Scorecards (Kaplan
and Norton, 1996).
Diagram classication and the method for the
choice of diagram helped RSC to identify the most
suitable visual knowledge codication techniques
in order to answer the aforementioned questions.
What is marketing?corresponds to the What is
it?question (Figure 1) WHAT-Knowledge
Mindmap (Buzan, 2006; Koznov et al., 2011) can be
effective for this knowledge according to Figure 2.
So a marketing mindmap (Figure 3)
was developed
as the basis for marketing capability deployment.
Clarication of the main marketing concepts
corresponds to such questions from Figure 1 as
What is the organization of marketing knowledge
domain?and What is the relationship between
entities?WHAT-knowledge Concept map
(Novak and Cañas, 2008) was chosen using
Figure 2 in order to explain the marketing concep-
tual structure; see the example in Figure 4.
What should the company do in the marketing
domain?was reworded as What are the
constituents (or parts) of the marketing function
(capability)?. This type of question can be found
in the list of competency questions that describes
WHAT-knowledge (Figure 1). WHAT-knowledge
All the client-specic data were eliminated from the diagrams
because of the non-disclosure agreement. This therefore consti-
tutes a generalized/reference model of the marketing domain.
Figure 2 Classication of visual knowledge codication techniques. *, these are universal techniques that can be applied to different
knowledge types, but are more frequently used for what-knowledge. [Colour gure can be viewed at]
Novel Classication of Visual Knowledge Codication Techniques 7
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
can be represented by domain-independent
diagrams: concept map, tree diagram, class
diagram, functions tree and so on (Figure 2). The
functions tree was selected to represent What
should the company do in the marketing domain?
(Figure 5).
What are the current problems and their causes in the
marketing domain?was reworded as What are the
causes or reasons for the situation/problem?from
Figure 1 WHY-knowledge. Table 1 also supports
the choice of this knowledge type, as the Problem
and Causeconcepts and the has causetype of
relationship are all associated with WHY-
knowledge. Figure 2 recommends cause-and-effect
or an Ishikawa diagram (Ishikawa, 1963; Kenett,
2007) for WHY-knowledge, and this was chosen
and created for the representation of problems and
their causes in RSC marketing (see Figure 6).
What are the objectives and performance metrics of
the marketing domain?focuses on the term
Objective, so Table 1 helps to associate this
question with WHAT FOR-knowledge. Figure 2
Figure 3 Mindmap for the marketing domain (WHAT-knowledge example). [Colour gure can be viewed at]
Figure 4 Concept map for the marketing domain (WHAT-knowledge example). [Colour gure can be viewed at]
8 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
suggests Strategy map(Kaplan and Norton,
2004), which was chosen and created (Figure 7).
Performance metrics were integrated into a
marketing Balanced Scorecard and represented in
table format.
A business process diagram was constructed
to help in answering How should RSC organize
its marketing processes?(Galloway, 1994), a
HOW-knowledge diagram. The list of RSC market-
ing processes included more than 50 elements, and
Figure 5 Function tree for marketing (WHAT-knowledge example). [Colour gure can be viewed at]
Figure 6 Ishikawa diagram for marketing (WHY-knowledge example). [Colour gure can be viewed at]
Novel Classication of Visual Knowledge Codication Techniques 9
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DOI: 10.1002/kpm
more than 15 processes were mapped. Figure 8
shows a sample process map.
How to establish marketing capability?led to a
project portfolio, which was organized into a
marketing implementation programme. This pro-
gramme was partially represented in tables
(Projects, Priorities, etc.) and graphically using
Gantt charts (Wilson, 2003). This information is not
presented here, because it is client specic.
All these diagrams not only work independently,
but also support each other. For example, Figures 3
and 4 provide a glossary for other diagrams; the
structure of marketing functions (Figure 5) helped
to organize primary marketing activity objectives
(Figure 7); the structure and contents of cause and
effect diagram (Figure 6) helped to describe
marketing-enabling system objectives (Figure 7);
the function tree (Figure 5) includes a Customer
satisfaction analysisfunction, which is supported
by Customer satisfaction analysis based on the
completed ordersprocess (Figure 8).
These visual knowledge representations sup-
ported RSC managers during their strategy-to-
execution process, helping them to articulate a
functional marketing strategy and link it with the
operating model; nally, the organizational develop-
ment programme was suggested. The chosen
diagram types were accepted by managers and
formed the corporate standard of RSC.
The use of diagramming techniques, which can be
selected by the proposed method, has its difculties
and limitations. One of the problematic issues in the
Figure 7 Strategy map for operational marketing (WHAT FOR-knowledge example). [Colour gure can be viewed at]
Figure 8 Business process diagram for customer satisfaction
analysis based on the completed orders (HOW-knowledge ex-
ample). [Colour gure can be viewed at]
10 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
use of diagrams is semantic interoperability:
different users may interpret the meanings of the
symbols in the diagram differently. Visual models
may have elements with similar names but with a
different meaning, and conversely, concepts with
similar meaning may have varying names and
signs. In order to resolve this problem, a unied
meta-model (Karagiannis and Höfferer, 2006;
Heidari et al., 2013) or ontology (Andersson et al.,
2006; Guizzardi et al., 2006; Grigoriev and
Kudryavtsev, 2011; Hinkelmann et al., 2015) can be
used. Additionally, consistent diagram examples,
developed in accordance with the chosen notation,
are useful (training dataset). The more, the better.
In this case, users will conceive the meaning of
notation elements through examples (extensional
denitions of concepts).
Business tasks usually need more than a single
diagram, in fact, a system of diagrams that cover
different knowledge types. There are methodologies
that provide visual languages for such purposes,
including the following: IDEF for information
system design and business process re-engineering
(Mayer et al., 1992), Goldratts theory of constraints
for continuous improvement (Dettmer, 1997),
CommonKADS for knowledge-based system design
(Schreiber et al., 2000), Archimate for enterprise archi-
tecture management (The Open Group, 2012;
Lankhorst et al., 2013), UML (Rumbaugh et al., 2004)
and MEMO (Frank, 2002) for design of information
systems. For example, IDEF includes a set of visual
sub-languages: IDEF0 is suitable for function model-
ling; IDEF1 is used for specifying entity relationships;
IDEF1X supports the design of relational databases;
IDEF3 captures the process description; IDEF4 spec-
ies object-oriented design; and IDEF5 captures the
ontology description (Mayer et al., 1992). If the task
of interest is supported by an existing modelling
methodology and there are resources for training,
then it would be reasonable to use the existing meth-
odology. Otherwise, light-weightapproach to the
selection of a visual knowledge codication tech-
nique will be preferable.
Finally, the proposed classication and method
do not address the choice of techniques for the
representation of relationships between different
knowledge types (e.g. how-whoknowledge
links). These relationships between perspectives
can be successfully supported by matrices or
by comprehensive diagrams (Grigoriev and
Kudryavtsev, 2013).
Nevertheless, diagramming is a useful tool for
knowledge codication, which can be extended in
some situations. In particular, diagramming
techniques must be grounded in ontologies or
meta-models in order to provide semantic interop-
erability. It is also better to use complex task-specic
or domain-specic methodologies, which combine
several diagram types, if they t the given task
and/or domain.
The main novelty of our approach is that a new sys-
temic view of business diagrams is proposed. The
proposed classication describes the mapping
between knowledge types and popular business
diagram types. The classication takes diagram-
ming techniques from the analysis of organizational
documents, templates in popular diagramming
tools and consulting experience. It is grounded in
the semantic analysis of knowledge types and
visual knowledge codication techniques, which
enable to suggest mapping between them. This
mapping, together with the suggested informal
descriptions of knowledge types, can help man-
agers to understand the potential of visual represen-
tations and choose appropriate and comprehensive
models. The suggested diagrams can themselves
be considered as diagram types, which may have
many variations and particular notations. We have
tried to extract the most generic or prototypical
inherent elements of diagramming techniques.
The suggested case study for the marketing
domain of a medium-sized repair service company
demonstrated the relevance and applicability of
our novel method for knowledge diagram
classication and selection.
The overall aim of this research is to improve visual
literacy among both business practitioners and edu-
cators. Its classication-based approach can be con-
sidered as the rst step towards visual literacy
among managers, which in its turn helps to achieve
information maturity. Visual literacy corresponds
with recent trends in enterprise modelling, in which
business diagrams describe components of the enter-
prise architecture (Lankhorst et al., 2013); according to
the Maturity Modelfor Enterprise Architecture
Representations (Polikoff and Coyne, 2005), ad hoc
visual models of enterprise architecture correspond
to the rst level of maturity.
It is hoped that the proposed new classication will
allow better practical use of visual knowledge maps
in business practice and lead to fewer professional
misunderstandings and more effective business com-
munication. This approach is radically different from
the non-systemic selection of classes and categories
proposed by the main vendors of diagramming soft-
ware. The anarchy of different models increases infor-
mation overload in our era, while the proposed
classication minimizes the selection set of diagrams
and may be used as a visual guide by practitioners.
The authors wish to thank Lev Grigoriev, Chief
Technology Ofcer of Business Engineering Group, for
his help in the demonstration of the suggested approach
using the case study.
This research was supported nancially by the Russian
Science Foundation grant (project No. 15-18-30048).
Novel Classication of Visual Knowledge Codication Techniques 11
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DOI: 10.1002/kpm
Alavi M, Leidner D. 2001. Knowledge management and
knowledge management systems: conceptual
foundations and research issues. MIS Quarterly 25(1):
Andersson B, Bergholtz M, Edirisuriya A, Ilayperuma T,
Johannesson P, Gordijn J, Hahn A. 2006. Towards a
reference ontology for business models. Lecture Notes
in Computer Science 4215: 482496.
Blackwell A, Engelhardt Y. 2002. A Meta-Taxonomy for
Diagram Research. In Diagrammatic Representation and
Reasoning, Anderson M, Meyer B, Olivier P (eds).
Springer: London; 4764.
Buzan T. 2006. Mind mapping. Pearson Education: Canada.
Cassell C, Symon G (eds). 2004. Essential Guide to
Qualitative Methods in Organizational Research. SAGE
Publications Ltd.
Dettmer HW. 1997. Goldratts Theory of Constraints: A
Systems Approach to Continuous Improvement. ASQ
Quality Press: Milwaukee, Wisconsin.
Eisenstadt M, Domingue J, Rajan T, Motta E. 1990. Visual
knowledge engineering. IEEE Transactions on Software
Engineering 16(10): 11641177.
Eppler M. 2008. A process-based classication of
knowledge maps and application examples. Knowledge
and Process Management 15(1): 5971.
Eppler M, Burkhard R. 2007. Visual representations in
knowledge management: framework and cases. Journal
of Knowledge Management 11(4): 112122.
Eppler M, Jianxin G. 2008. Communicating with
Diagrams: how Intuitive and cross-cultural are Business
Graphics? Euro Asia Journal of Management 18(35): 322.
Frank U. 2002. Multi-perspective enterprise modeling
(memo) conceptual framework and modeling lan-
guages. In Proceedings of Hawaii International
Conference on System Sciences (HICSS); 12581267.
Galloway D. 1994. Mapping Work Processes. ASQ Quality
Press: Milwaukee, Wisconsin.
Gangemi A, Presutti V. 2009. Ontology Design Patterns. In
Handbook on Ontologies, Staab S, Studer R (eds). Springer
Berlin Heidelberg: Berlin, Heidelberg; 221243.
Gavrilova T, Voinov A. 1998. Work in progress: Visual
specication of knowledge bases. In Tasks and Methods in
Applied Articial Intelligence. Springer: Berlin Heidelberg;
Gavrilova TA, Gulyakina NA. 2011. Visual knowledge
processing techniques: a brief review. Scientic and
Technical Information Processing 38(6): 403408.
Glassey O. 2008. Method and instruments for modeling
integrated knowledge. Knowledge and Process Manage-
ment 15(4): 247257.
Grigoriev L, Kudryavtsev D. 2011. Ontology-based
business architecture engineering framework. Frontiers
in Articial Intelligence and Applications 231: 233252.
Grigoriev L, Kudryavtsev D. 2013. Non-diagrammatic
method and multi-representation tool for integrated en-
terprise architecture and business process engineering.
In Proceedings of 2013 IEEE International Conference
on Business Informatics, Vienna, Austria; 216221.
Gruber TR. 1995. Toward principles for the design of
ontologies used for knowledge sharing? International
Journal of Human-Computer Studies 43(5): 907928.
Gruninger M, Fox M. 1995. Methodology for the design
and evaluation of ontologies. In Proceedings of IJCAI
1995 Workshop on Basic Ontological Issues in
Knowledge Sharing, Montreal, Canada; 6.16.10.
Guizzardi G, Pires L, van Sinderen M. 2006. Ontology-
based evaluation and design of domain-specic visual
modeling languages. In Advances in Information Systems
Development. Springer: US; 217228.
Harel D, Rumpe B. 2000. Modeling Languages: Syntax,
Semantics and All That Stuff, Part I: The Basic Stuff.
Hauser JR, Clausing D. 1988. The house of quality.
Harvard Business Review 66(3): 6373.
Heidari F, Loucopoulos P, Brazier F, Barjis J. 2013. A meta-
meta-model for seven business process modeling
languages. In Proceedings of 15th Conference on
Business Informatics (CBI); 216221.
Hevner A, Chatterjee S. 2010. Design Research in
Information Systems: Theory and Practice. Springer
Science & Business Media: Springer US.
Hinkelmann K, Gerber A, Karagiannis D, Thoenssen B,
van der Merwe A, Woitsch R. 2015. A New Paradigm
for The Continuous Alignment of Business and IT:
Combining Enterprise Architecture Modelling and
Enterprise Ontology. Computers in Industry. Article from
Elsevier journal.
Hodgkinson GP, Maule AJ, Bown NJ. 2004. Causal cogni-
tive mapping in the organizational strategy eld: a
comparison of alternative elicitation procedures.
Organizational Research Methods 7(1): 326.
Huff AS. 1990. Mapping Strategic Thought. John Wiley & Sons.
Ishikawa K. 1963. Cause and effect diagram. In Proceed-
ings of International Conference on Quality.
Kaplan RS, Norton DP. 1996. The Balanced Scorecard:
Translating Strategy into Action. Harvard Business Re-
view Press.
Kaplan RS, Norton DP. 2004. Strategy Maps: Converting
Intangible Assets into Tangible Outcomes. Harvard Busi-
ness Review Press.
Karagiannis D, Höfferer P. 2006. Metamodeling as an Inte-
gration Concept. In Software and Data Technologies.
Springer: Berlin Heidelberg; 3750.
Kenett RS. 2007. Cause-and-Effect Diagrams. Wiley StatsRef:
Statistics Reference Online. John Wiley & Sons, Ltd.
Kingston J, Macintosh A. 2000. Knowledge management
through multi-perspective modelling: representing
and distributing organizational memory. Knowledge-
Based Systems 13(2): 121131.
Kingston J, Grifth A, Lydiard T. 1997. Multi-perspective
modelling of the air campaign planning process. In Pro-
ceedings of AAAI-96, AAAI Press, 1996, Menlo Park,
California, USA; 668677.
Kirschner PA, Buckingham-Shum SJ, Carr CS (eds). 2003.
Visualizing Argumentation: Software Tools for Collaborative
and Educational Sense-making. Springer Science &
Business Media: Springer-Verlag London.
Koznov D, Larchik E, Pliskin M, Artamonov N. 2011.
Mind maps merging in collaborative work. Program-
ming and Computer Software 37(6): 315321.
Kudryavtsev D, Gavrilova T, Leshcheva I. 2013. One
approach to the classication of business knowledge
diagrams: practical view. In Proceedings of IEEE 2013
Federated Conference on Computer Science and
Information Systems (FedCSIS), Krakow, Poland;
Lankhorst M et al. 2013. Enterprise Architecture at Work:
Modelling, Communication and Analysis (The Enterprise
Engineering Series). Springer-Verlag: Berlin Heidelberg,
Third Edition.
Lengler R, Eppler M. 2007. Towards a periodic table of
visualization methods for management. In Proceedings
of the Conference on Graphics and Visualization in
Engineering; 16.
Lohse G, Biolsi K, Walker N, Rueter H. 1994. A
classication of visual representations. Communications
of the ACM 37(12): 3649.
Maedche A, Motik B, Stojanovic L, Studer R, Volz R. 2003.
Ontologies for enterprise knowledge management.
IEEE Intelligent Systems 18(2): 2633.
12 D. Kudryavtsev and T. Gavrilova
Copyright © 2016 John Wiley & Sons, Ltd. Know. Process Mgmt. 24,313 (2017)
DOI: 10.1002/kpm
Mayer RJ, Painter MK, deWitte PS. 1992. IDEF Family of
Methods for Concurrent Engineering and Business Re-
engineering Applications. Knowledge Based Systems,
Inc: College Station, TX.
Novak JD, Cañas AJ. 2008. The theory underlying concept
maps and how to construct and use them.
Peffers K, Tuunanen T, Rothenberger M, Chatterjee S.
2008. A design science research methodology for
information systems research. Journal of Management
Information Systems (JMIS) 24(3): 4577.
Phaal R, Farrukh CJ, Probert DR. 2006. Technology
management tools: concept, development and applica-
tion. Technovation 26(3): 336344.
Polikoff I, Coyne RF. 2005. Towards executable enterprise
models: ontology and semantic web meet enterprise
architecture. Journal of Enterprise Architecture,
Fawcette Publications. Available from: http://www.
[Accessed 23 February 2016].
Rasmusson D. 2006. The SIPOC Picture Book: A Visual
Guide to the SIPOC/DMAIC Relationship. Oriel Incorpo-
rated: Madison, WI.
Ren Y, Parvizi A, Mellish C, Pan J, Van Deemter K,
Stevens R. 2014. Towards competency question-driven
ontology authoring. In The Semantic Web: Trends and
Challenges, Presutti V, dAmato C, Gandon F, dAquin
M, Staab S, Tordai A (eds). Lecture Notes in Computer
Science. Springer International Publishing 8465: 752
Rodríguez MA, Egenhofer MJ. 2003. Determining
semantic similarity among entity classes from different
ontologies. IEEE Transactions on Knowledge and Data
Engineering 15(2): 442456.
Rumbaugh J, Jacobson I, Booch G. 2004. The Unied Model-
ing Language Reference Manual, Second edition. Pearson
Higher Education, Addison-Wesley Professional.
Saunders M, Lewis P, Thornhill A. 2011. Research Methods
for Business Students. Financial Times Prentice Hall:
London. In RICS Construction and Property
Schreiber G, Akkermans H, Anjewierden A, Hoog R,
Shadbolt N, Wielinga B. 2000. Knowledge Engineering and
Management: The CommonKADS Methodology. MIT press:
Cambridge, Massachusetts.
Smith M, Erwin J. 2005. Role & Responsibility Charting
(RACI). Available from:
downloads/raci_r_web3_1.pdf [Accessed 7 February 2016].
Stock WG. 2010. Concepts and semantic relations in infor-
mation science. Journal of the American Society for Infor-
mation Science and Technology 61(10): 19511969.
The Open Group. 2012. ArchiMate 2.1 Specication. The
Open Group Publications Catalog, 20122013. Avail-
able from:
archimate2-doc/ [Accessed 23 February 2016].
Wilson JM. 2003. Gantt charts: A centenary appreciation.
European Journal of Operational Research,149(2): 430437.
Zachman J. 2003. The Zachman framework for enterprise
architecture: a primer for enterprise engineering and
Novel Classication of Visual Knowledge Codication Techniques 13
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DOI: 10.1002/kpm
... Codification captures articulated tacit knowledge making it explicit and thereby transforming it into information that can be transferred, disseminated and archived (Ancori et al., 2000;Håkanson, 2007;Kotlarsky et al., 2014;Kudryavtsev and Gavrilova, 2017). ...
... Codification is variously defined as 'the process of conversion of knowledge into messages which can then be processed as information' (Cowan and Foray, 1997, p. 596); 'the means by which knowledge is made explicit and hence readily stored or transferred between groups' (Kotlarsky et al., 2014, p. 609); 'the transformation of knowledge into information' (Ancori et al., 2000, p. 256); and 'the expression of knowledge in a standardized, fixed form ' Håkanson (2007, p. 61). Codification captures articulated knowledge making it as explicit and portable as possible in order to maximize its accessibility (Kudryavtsev and Gavrilova, 2017). Codification also captures and categorizes knowledge into explicit formats allowing the transfer, diffusion, storage and ready availability of knowledge (Håkanson, 2007). ...
... As discussed previously, codification conveys ideas, facts and processes and assists in creating shared understanding (Kudryavtsev and Gavrilova, 2017). Due to increased tangibility and ease of access, codified knowledge positively affects rates of knowledge creation and innovation, directly affecting the generation and distribution of tacit knowledge (Cohendet and Steinmueller, 2000;Kotlarsky et al., 2014), thereby reducing dependencies between diversified groups (Vaast and Levina, 2006). ...
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This chapter offers insight into how knowledgeKnowledgecodificationCodification has the potential to capture varying levels of tacitTacit content in pursuit of enhanced innovation and ultimately, competitive advantageCompetitive advantage. It recognizes the need for and importance of codifying knowledge in a variety of ways to allow codified material to move from lower to higher explicit content. We propose that newly articulated knowledge is codified immediately and that informal and formal codificationCodification exercises create boundary objects that can be made more complete or explicit through critique, review and reflection on these codified materials. This approach also enhances employee engagementEmployee engagement in the codification/re-codification processCodification process. The value of involving operators in codification, using existing codebooksCodebooks, optimizes articulationArticulation and codification by inviting a focused review of existing codified material and the subsequent standardization of codified knowledgeCodified knowledge: it also releases new tacit knowledgeTacit knowledge within a forum designed to immediately codify this newly articulated knowledge. The proposed knowledgeKnowledgecodificationCodification framework offers users the potential to apply the principles exhibited in this chapter in practice.
... In the digital age, it is necessary to employ new instruments and methods to increase the effectiveness and timeliness of company management actions (Song et al., 2019). Nowadays, a growing number of managers rely on visualization tools, such as visual metaphors, sketches, knowledge diagrams and maps (or other systems for big data visualization) (Bresciani et al., 2014;Berinato, 2016a, b;Kudryavtsev and Gavrilova, 2017;Castellano and Del Gobbo, 2018;Chinnaswamy et al., 2019;Gavrilova et al., 2019). These tools translate information into visuals and have become increasingly used in many organizations in recent years (Berinato, 2016b). ...
... In the last few years, a growing number of visualizations tools have emerged as leading examples of knowledge visualization. Among these, the most important are: heuristic sketches, conceptual diagrams, visual metaphors, knowledge maps, immersive three-dimensional (3D) environments Lurie and Mason, 2007;Eppler and Bresciani, 2013;van Biljon and Renaud, 2015;Berinato, 2016a, b;Kudryavtsev and Gavrilova, 2017;Aas and Alaassar, 2018;Gavrilova et al., 2019). These visualization formats are more advanced and performing than in the past and are fundamental for managers these days, in fact, as pointed out by Gavrilova et al. (2017, p. 8) "modern knowledge management is inconceivable without extensive use of diagrams, graphics and schemas." ...
... Recently, Kudryavtsev and Gavrilova (2017) propose a classification of visual knowledge codification techniquesbased on seven categories of knowledge (i.e. what, how, who, why, when, where and what for)for multi-perspective business systems design and analysis. ...
Purpose-The purpose of this paper is to explore the main benefits and risks of knowledge visualization in the current digital age. Design/methodology/approach-The paper is based on a qualitative and explorative research to frame the benefits and risks of knowledge visualization. The emerging views of 57 small and medium-sized entrepreneurs (SMEs) managers are examined. Findings-The findings reveal both benefits and risks related to knowledge visualization. The two aggregate dimensions (i.e. benefits and risks) are supported by six second-order and five second-order categories, respectively. On one side, the main benefits highlighted in the study are related to: stakeholder engagement, flexibility, knowledge transfer, signaling role, agility and interactivity; on the other side, the risks identified are related to: complexity, absorptive capacity, divergences, capabilities and ineffectiveness. Originality/value-The research highlights novel insights in the emerging field of knowledge visualization and extends current literature. It provides useful implication from both a theoretical and practical point of view.
... Natural-language texts are more expressive and well-known for humans but contain significant semantic noise [1,2,14]. Graphical form stimulates creative thinking but has limited expressive capabilities and requires learning of the specific notation [12]. Formal languages for representing knowledge like OWL2, Prolog, and others allow computer processing of knowledge (logical reasoning and contradiction detection) but they are too complicated for human perception [13]. ...
... WHAT-knowledge consists of concepts and relations between them so it can be naturally presented as a graph. As visualization stimulates human thinking [12], the most promising intermediate models for ontology development and understanding are well-known diagrams: mind maps, concept maps, conceptual graphs, UML class diagrams, ER diagrams (in different notations), ORM2 diagrams and so on. There are known approaches to using these diagrams for ontological engineering [3,8,9,15,18]. ...
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Creating and understanding ontologies using OWL2 language is a hard, time-consuming task for both domain experts and consumers of knowledge (for example, teachers and students). Using Object-Role Modeling diagrams as an intermediate model facilitates this process. To achieve this, the method of mapping ORM2 diagrams to OWL2 ontologies and vice versa is necessary. Such methods were proposed in different works, but their suitability and possible errors are in doubt. In this paper, we propose a method of evaluating how well existing rules of mapping follow ORM semantics. Several ontologies were created using mapping rules and tested. During testing, a significant difference between ORM2 and OWL2 basic properties and assumptions were discovered. This difference require updating the mapping rules.
... The content management lifecycle comprise a few phases. Smith and McKeen (2003) proposes four lifecycle phases: (a) capturing content involving all activities associated with (codified) content collection, (b) organising content through indexing, classification, versioning, archiving and real-time routing/ delivery of content allowing access within and across teams/functional units (Kudryavtsev & Gavrilova, 2017), (c) processing, that is, sifting and analysing content in ways that inform decision-making, and (d) maintenance, that is, ensuring that content is in the form of mechanisms to retrieve, present, publish, visualize, and personalize content (Gupta et al., 2001). ...
Firm‐wide integrated organisational applications such as intranets, enterprise portals, content repositories, and wikis are often instrumental in supporting knowledge work. Serving as centralised “containers” of codified content that facilitate knowledge work, one essential requirement is continuous reuse and management of codified content. In this paper, our investigation aims to determine suitable perspectives to manage codified content, by focusing on one specific integrated organisational infrastructure application, namely intranets. Findings from three case studies identified three modes to manage codified content for knowledge work for enterprise content systems and are: (a) shared, (b) controlled, and (c) informal. In addition, based on the three modes (or ways), findings also highlight four elements that impact the management of codified content for knowledge work, that is, (a) content contributions, (b) content sharing, (c) access to expertise, and (d) control of codified content.
Purpose Managing a project involves taking a number of critical decisions that can have a crucial impact on the success or failure of the initiative. The analytical definition and visualization of the main components of a project can support project managers engaged to address the right issues at the right time. This article aims to identify crucial crossroads in the management of a project and to provide a visual representation of knowledge involved into a system of project components and decisions. Design/methodology/approach A design science process is adopted to define the initial goals and requirements and to develop the knowledge visualization framework. Expert feedback is also gathered to obtain a preliminary validation of the framework. Findings Moving from a system view of project dimensions, we identify eight types of strategic decisions, i.e. growth, problem shifting, goals balancing, escalation, rewarding, resource allocation, problem fixing and cooperation. We then present a visualization map of project decision making addressing six categories of knowledge (i.e. “what-knowledge”, “how-knowledge”, “who-knowledge”, “why-knowledge”, “what for-knowledge”, “when-knowledge”). Research limitations/implications The framework needs further theoretical refinement in terms of more fine-grained decision types, other determinants and the reciprocal influence in the management of project activities. Practical implications The article can support project managers attempting to build a comprehensive view of project decisions, and it can be a basis to develop novel types of knowledge management systems for project-related applications. Originality/value The article proposes a new approach to sustain strategic decision making in project management by adopting a knowledge visualization view. Moreover, it provides an operational tool for managers and analysts at different levels engaged into the management of a project.
Purpose Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process. Design/methodology/approach The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation. Findings Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development). Research limitations/implications The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners. Practical implications This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories. Originality/value This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.
Structure of the codified knowledge matters: it defines knowledge transferability while its absence creates information overload. Knowledge transferability creates organizational value through an increase of intellectual capital, innovative activity, and firm performance. The knowledge‐based view postulates higher transferability of codified knowledge, but the codification approach complies with a set of contradictions. The present empirical study uncovers nonlinear relationships between knowledge structure and knowledge transferability and encourages further studies toward search for optimal knowledge structure.
Purpose The paper aims to propose a knowledge visualization approach and algorithm to support public decision makers to define the inner areas, which represents a strategic topic in the European debate about territorial inequality and development. Design/methodology/approach The study has been developed by following the design science research, which includes six steps: problem identification and motivation; identification of the objectives for a solution; design and development; demonstration; evaluation; and communication. As for the design and development step, the proposed approach and algorithm ground on association mining to discover hidden relationships existing among municipalities. They have been applied to analyse the 97 municipalities of the Lecce province, and each municipality has been described through 30 multi-domain indicators organized into seven categories, whose data have been collected from institutional datasets, local sources or web-scraping process. Findings A set of complementary analyses has been generated through the construction of dynamic and interactive knowledge maps that show “similar” municipalities according to the indicators selected. Originality/value The approach and algorithm proposed allow discovering similarities existing among distinct municipalities, based on the analysis of a set of multi-domain indicators. The approach may complement or completely substitute the existing ones used to define inner areas, thus overcoming both the methodological limits of the “top-down” line imposed by the central legislator, and the “bottom-up” paradox consisting in the illusion that single (and often small) towns have the economic and cognitive resources necessary to implement effective territorial mapping and development strategies. In such a way, policy makers can be aware on similarities existing among distinct towns and can thus share cognitive and financial resources to define a common plan and a set of practices for territorial development.
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An enterprise architecture tries to describe and control an organisation?'s structure, processes, applications, systems and techniques in an integrated way. The unambiguous specification and description of components and their relationships in such an architecture requires a coherent architecture modelling language. Lankhorst and his co-authors present such an enterprise modelling language that captures the complexity of architectural domains and their relations and allows the construction of integrated enterprise architecture models. They provide architects with concrete instruments that improve their architectural practice. As this is not enough, they additionally present techniques and heuristics for communicating with all relevant stakeholders about these architectures. Since an architecture model is useful not only for providing insight into the current or future situation but can also be used to evaluate the transition from ?as-is? to ?to-be?, the authors also describe analysis methods for assessing both the qualitative impact of changes to an architecture and the quantitative aspects of architectures, such as performance and cost issues. The modelling language and the other techniques presented have been proven in practice in many real-life case studies. So this book is an ideal companion for enterprise IT or business architects in industry as well as for computer or management science students studying the field of enterprise architecture. © Springer-Verlag Berlin Heidelberg 2005. All rights are reserved.
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The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.
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Business architecture became a well-known tool for business transformations. According to a recent study by Forrester, 50 percent of the companies polled claimed to have an active business architecture initiative, whereas 20 percent were planning to engage in business architecture work in the near future. However, despite the high interest in BA, there is not yet a common understanding of the main concepts. There is a lack for the business architecture framework which provides a complete metamodel, suggests methodology for business architecture development and enables tool support for it. The ORG-Master framework is designed to solve this problem using the ontology as a core of the metamodel. This paper describes the ORG-Master framework, its implementation and dissemination.
Computer Supported Argument Visualization is attracting attention across education, science, public policy and business. More than ever, we need sense-making tools to help negotiate understanding in the face of multi-stakeholder, ill-structured problems. In order to be effective, these tools must support human cognitive and discursive processes, and provide suitable representations, services and user interfaces. Visualizing Argumentation is written by practitioners and researchers for colleagues working in collaborative knowledge media, educational technology and organizational sense-making. It will also be of interest to theorists interested in software tools which embody different argumentation models. Particular emphasis is placed on the usability and effectiveness of tools in different contexts. Among the key features are: - Case studies covering educational, public policy, business and scientific argumentation - Expanded, regularly updated resources on the companion website: "The old leadership idea of "vision" has been transformed in the face of wicked problems in the new organizational landscape. In this excellent book we find a comprehensive yet practical guide for using visual methods to collaborate in the construction of shared knowledge. This book is essential for managers and leaders seeking new ways of navigating complexity and chaos in the workplace." (Charles J. Palus, Ph.D, Center for Creative Leadership, Greensboro, North Carolina, USA)
Conference Paper
Ontology authoring is a non-trivial task for authors who are not proficient in logic. It is difficult to either specify the requirements for an ontology, or test their satisfaction. In this paper, we propose a novel approach to address this problem by leveraging the ideas of competency questions and test-before software development. We first analyse real-world competency questions collected from two different domains. Analysis shows that many of them can be categorised into patterns that differ along a set of features. Then we employ the linguistic notion of presupposition to describe the ontology requirements implied by competency questions, and show that these requirements can be tested automatically.
The paper deals with Next Generation Enterprise Information Systems in the context of Enterprise Engineering. The continuous alignment of business and IT in a rapidly changing environment is a grand challenge for today's enterprises. The ability to react timeously to continuous and unexpected change is called agility and is an essential quality of the modern enterprise. Being agile has consequences for the engineering of enterprises and enterprise information systems. In this paper a new paradigm for next generation enterprise information systems is proposed, which shifts the development approach of model-driven engineering to continuous alignment of business and IT for the agile enterprise. It is based on a metamodelling approach, which supports both human-interpretable graphical enterprise architecture and machine-interpretable enterprise ontologies. Furthermore, next generation enterprise information systems are described, which embed modelling tools and algorithms for model analysis.
A k-out-of-n system is a system with n components which functions or works if at least k out of the n components work. Parallel and series systems are special cases of a k-out-of-n system. There are many different arrangements of the components in a k-out-of-n system and many assumptions that can be made about the lifetimes of the components. In this chapter, the lifetimes of the components in the system are assumed to be independent and identically distributed. The reliability function of the system is found by calculating the probability that at least k components out of the n components work. The mean time to failure of the system is also found for the case of components with exponential distributions of lifetimes. In addition, the reliability function for a linear and circular consecutive k-out-of-n system is given.Keywords: k-out-of-n system;reliability;exponential distribution;consecutive k-out-of-n system;circular k-out-of-n system;linear k-out-of-n system;binomial distribution