Repurposing Zachman Framework Principles for

"Enterprise Model"-Driven Engineering

Alisa Harkai, Mihai Cinpoeru and Robert Andrei Buchmann

Business Informatics Research Center, Babeş-Bolyai University, str. T. Mihali 58-60, Cluj Napoca, Romania

Keywords: Enterprise Modelling, Enterprise Knowledge Engineering, Zachman Framework, Resource Description

Framework.

Abstract: The paper proposes an agile modelling tool which implements a domain-specific modelling method. As a

motivational starting point for the development of this modelling tool, we employ the Zachman Framework -

an ontology which conceptualises an enterprise across a variety of abstractions and facets. We conducted our

work with respect to the Zachman Framework in order to cover several of these facets and to suggest the

possibility of further employing Agile Modelling Method Engineering to extend this coverage, with the tool

providing the ability to create hyperlinks between models expressing different enterprise views. The agile

modelling tool developed as a proof-of-concept is further coupled with semantic technology to make models

available to semantic queries and machine reasoning in the context of model-driven software engineering.

1 INTRODUCTION

Motivated by project-based requirements (the

EnterKnow project (Enter Know, 2017)), we

analysed the Zachman Framework (ZF) as a

motivational starting point for implementing an

enterprise modelling method. Although ZF is

commonly presented as an ontology, it is not one in

the formal sense employed in ontology engineering

(as promoted in the works of (Smith, 2004) or

(Guarino, 1995)). Also, it is not truly a methodology

(Sessions, 2007), so it cannot be considered a fully-

fledged modelling method in the sense defined by

(Karagiannis and Kühn, 2002). It is a bi-dimensional

schema that can guide enterprise architects and

enterprise architecture managers in making decisions

based on definition, design and analysis of

architectural information for enterprises and their

information systems (Zachman, 1982).

Consequently, we aimed to investigate how ZF's

principles can be grounded in more formal and

actionable knowledge structures, with respect to

software engineering needs. More precisely, we aim

to derive machine-readable knowledge from a ZF-

guided enterprise architecture design, in a way that

can further support knowledge-driven software

development processes for Enterprise Information

Systems. Our investigation identified several

opportunities and paradigms developed during recent

years, whose interplay supports our goal: (i) the

Multi-perspective Enterprise Modelling paradigm

which advocates the description of an enterprise

through multiple models reflecting different facets of

the same enterprise (Kingston and Macintosh, 2000)

(Frank, 2000); (ii) the Agile Modelling Method

Engineering (AMME) framework (Karagiannis,

2015) which enables the full customization and quick

prototyping of multi-view modelling tools according

to situation-specific requirements – see an example in

(Bork, 2015); (iii) the Linked Open Models proposal

(Karagiannis and Buchmann, 2016) which advocates

the serialization of diagrammatic models in RDF

knowledge graphs (W3C, 2017a) in order to make

diagrammatic content available to running

knowledge-based systems outside a modelling

environment.

Based on this investigation, we advocate the

possibility of coupling the design thinking suggested

by ZF's conceptual frame with semantic technology

that can expose it to semantic queries and machine

reasoning. In support of this proposal, we developed

a proof-of-concept agile enterprise modelling tool

that reflects the multi-faceted nature of ZF while

establishing machine-readable semantic links

governed by an overarching meta-model to expose

the heterogeneous customised models as machine-

readable knowledge graphs. One current limitation

682

Harkai, A., Cinpoeru, M. and Buchmann, R.

Repurposing Zachman Framework Principles for "Enterprise Model"-Driven Engineering.

DOI: 10.5220/0006710706820689

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 682-689

ISBN: 978-989-758-298-1

due to project requirements and limited scope is that

not all of ZF's perspectives have been deployed in the

proof-of-concept, only a selected subset correlated

with the requirements for a model-driven software

artefact. The software engineering method employed

to develop model-driven software artefacts based on

our tool is discussed in a different paper (Buchmann

et al., 2018).

This paper is structured as follows: Section 2

provides background information about ZF, the

AMME methodology and the RDF technological

space. Section 3 comments on related works. Section

4 discusses design decisions. Section 5 presents the

proof-of-concept agile enterprise modelling tool and

the final Section 6 provides conclusions based on a

SWOT analysis.

2 BACKGROUND,

METHODOLOGY AND

ENABLERS

2.1 Motivation: The Zachman

Framework

ZF is an enterprise ontology prescribing a

fundamental structure for Enterprise Architecture –

i.e., a formal and structured way of viewing and

defining an enterprise (Zachman, 2008). The

representation of this ontology is often in the form of

a 6x6 matrix, a two-dimensional schema used in order

to represent and organise the architectural layers and

artefacts comprising an enterprise system.

Figure 1 shows a representational form of this

framework in which the columns represent the ZF

facets and the rows represent the types of

stakeholders: (i) execution perspective (contextual

level); (ii) business management perspective

(conceptual level); (iii) architect perspective (logical

level); (iv) engineer perspective (physical level); (v)

technician perspective (as built); (vi) enterprise

perspective (functioning).

The core idea is that an asset can be described in

different ways for different purposes using those

levels of abstraction and views. This has also been

recognised in multi-view enterprise modelling (Bork,

2015) – the principle can be transferred to the practice

of Agile Modelling Method Engineering with the goal

of producing modelling tools that are customised to

capture, in a semantically integrated way, the facets

of ZF or enterprise assets pertaining to those facets.

Figure 1: Zachman’s Framework abstraction overview.

2.2 Methodology: The Agile Modelling

Method Engineering

Agile Modelling Method Engineering (Karagiannis,

2015) can be considered a Design Science (Peffers et

al., 2007) approach that is specialised for the

realisation of artefacts such as modelling methods and

modelling tools, regardless of application domain and

abstraction level. It advocates the ability of agilely

evolving the modelling semantics, syntax or

functionality with the help of meta-modelling

environments such as ADOxx (BOC Group, 2017). In

AMME, agility applies on two levels: (i) artefact

agility – which means that a modelling method is

decomposed in manageable building blocks of

various granularities (i.e., modelling language,

mechanisms and algorithms, modelling procedure)

and (ii) methodological agility which refers to the

actual engineering process - an incremental and

iterative development cycle in 5 steps: Create,

Design, Formalise, Develop and Deploy – see Figure

2, adapted from (Karagiannis, 2015) and subsequent

presentations (OMiLAB, 2017).

Figure 2: AMME lifecycle.

Nowadays, the notions of Agile Enterprise

(Goldman et al. 1994) and Agile Knowledge

Management (Levy and Hazzan, 2008) are commonly

adopted in management practices and Enterprise

Repurposing Zachman Framework Principles for "Enterprise Model"-Driven Engineering

683

Architecture Management could embrace a certain

level of agility in its model-based knowledge

repositories, one that can be synchronised with

software engineering processes. With the AMME

framework, agile modelling methods reflect the fact

that different purposes for different scopes must be

addressed due to the diversity of domains and

requirements. To this, the Linked Open Models vision

proposed in (Karagiannis and Buchmann, 2016) adds

the opportunity of exposing agile models to

knowledge-driven information systems, with the help

of the Resource Description Framework.

2.3 Technological Enablers: The

Resource Description Framework

The Resource Description Framework (RDF) (W3C,

2017a) is a World Wide Web Consortium standard

originally designed as a metadata model and evolved

as a technological foundation for the Semantic Web.

It can be used as a data model for conceptual

descriptions of Web resources in Linked Data

environments and coupled with ontologies.

The core idea of this data model is to capture

knowledge as statements about Web resources, in the

form of triples (subject-predicate-object). The subject

represents the resource and the predicate asserts a

relationship between the subject and the object or an

attribute whose value is the object. A collection of

RDF statements forms a directed multi-graph that can

be managed with the help of graph databases – e.g.

(Ontotext, 2017) - and queried through SPARQL

queries (W3C, 2017b). The Linked Open Models

vision (Karagiannis and Buchmann, 2016) introduced

several patterns and a plug-in for converting

diagrammatic models of arbitrary types to RDF - this

is employed in the work at hand for the ZF-driven

modelling tool hereby discussed.

3 RELATED WORKS

ZF is a well-established guide for structuring

enterprise knowledge. It was originally used for the

purpose of business system planning and it is

discussed under multiple interpretations by managers,

designers, programmers and other types of

stakeholders. This framework, designed as a schema,

prescribes facets and perspectives on enterprise

descriptions – however it does not specify the means

of bridging them in machine-oriented ways, and how

to further expose those bridges to external processing.

During the ’90s several frameworks extended ZF

- e.g., Evernden, The Integrated Architecture

Framework (Schekkerman, 2003). ZF is also used to

map various processes relevant to enterprise

architectures - e.g., analysis of the Rational United

Process (DJ de Villiers, 2001), Model-driven

architecture (Frankel et. al., 2003), TOGAF (The

Open Group, 2017a).

This paper's proposal tries to unify and combine

ZF, AMME and Linked Open Models towards the

goal of increasing the value and reusability of

enterprise architecture knowledge, opening the

possibility of interoperability with model-driven

information systems. AMME is used to partition with

appropriate granularity a custom-made modelling

language following the guidance of ZF's conceptual

frame and to agilely implement this in a modelling

tool. Then the Linked Open Models approach is

employed to make the resulting structure (and any

models built on it) available to semantic information

systems. The properties of enterprise artefacts

conceptualised in models can be navigated through a

dereferencing mechanism (originally presented in

(Cinpoeru, 2017) with application to BPMN) or

through SPARQL queries.

Enterprise knowledge often relies on RDF multi-

graphs coupled with ontologies (Teyeb et. al., 2015).

Modelling languages and conceptual patterns driven

by domain-specific requirements are also becoming

more prominent (Brambilla and Umuhoza, 2017)

(Galkin et. al., 2017). However, there is little work in

bridging all these paradigms towards a richer model-

based semantic support for enterprise information

systems.

4 DESIGN DECISIONS

The modelling tool to be described in this paper was

developed to combine the benefits of ZF, AMME and

RDF. By combining these, we want to show the

possibility of making a multi-perspective conceptual

frame available to semantic queries. Cross-

perspective links are governed by a meta-model

which enables the representation of enterprise

knowledge as machine-readable knowledge graphs

covering the facets of ZF that are deemed relevant for

a particular purpose.

Figure 3 shows examples of mappings between

ZF's facets and enterprise modelling symbols from

two well-known languages – Archimate (Open

Group, 2017b) and EEML (Carlsen, 1998).

The work at hand takes a similar mapping as a

starting point and specifies granular semantic links

across the ZF facets that can be exported as bridges

between RDF multigraphs. To develop the modelling

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684

Figure 3: Examples of diagrammatic symbols in modelling

languages reflecting the ZF facets.

tool, we started from creating a class diagram which

represents the meta-model governing the modelling

language, which can be evolved by applying the

AMME methodology as new requirements are

adopted (see Figure 4 for a snapshot of the meta-

model).

The meta-model was implemented using the

ADOxx platform (BOC Group, 2017), which

provides some abstract classes (e.g., __D-

construct__, __D-resource__, __D-container__ etc.)

as a starting point for the multi-perspective modelling

language. The class __D-construct__ represents the

root class for every meta-model and all the other

abstract or non-abstract classes inherit from it. As

predefined abstract classes, we also used the classes

__D-resource__, __D-container__ and __D-

aggregation__. The meta-model contains so far three

types of diagrams: (i) Model of courier (make-to-

order) processes, (ii) Model of locations and (iii)

Model of participants.

These three types of models/diagrams are linked

to each other by hyperlinks, the tasks require parkings

in cities or regions and also can have assigned roles

or employees.With these three types of models we

tried to cover the following ZF facets: (i) When/How,

(ii) Where and (iii) Who, with symbols indicated in

Figure 5.

Figure 4: Meta-Model (as Class Diagram).

Because of the project’s limited scope, not all of

ZF's facets have yet been implemented, but this target

could be reached with the help of AMME and ADOxx

platform, which allows the addition of new types of

models in order to extend the actual meta-model to

cover all ZF facets. Across these facets, hyperlinks

such as those highlighted in Figure 6 can be

established, distinguished by their semantics

according to the metamodel. Different kinds of links

are visible: The task Deliver has as assigned

employee the instance Jim or, for convenience, the

reverse link: Jim is responsible for doing the task

Deliver; the employee Jim can fulfil the role Big Car

Driver (roles can also be assigned to tasks, thus

delegating the instance assignment to some external

workflow

management system); the task Deliver

Repurposing Zachman Framework Principles for "Enterprise Model"-Driven Engineering

685

Figure 5: Examples of symbols and corresponding ZF

facets.

Figure 6: Linked model fragments.

requires parking in one of the cities, in this case City

A which contains two parkings areas of different

types.

All the models developed in the implemented

modelling tool can be exported and interpreted as

machine-readable content (RDF knowledge graphs).

RDF multi-graphs are used to represent the

knowledge, in this case the modelled enterprise

knowledge, and can be written in different

serialization syntaxes, e.g.: TriG (W3C, 2017c);

Turtle (W3C, 2017d); RDF/XML (W3C, 2017e). A

graphical representation of the model fragments and

links in Figure 6 is shown in Figure 7, including the

types derived from the metamodel (Figure 4).

Figure 7: Machine-readable knowledge graph.

5 PROOF-OF-CONCEPT

For developing the agile modelling tool we adopted

the case of a transport company that needs its courier

processes mapped on human resources and

geographical coverage, including instance processes

whose tasks can be assigned in the modelling

environment to instance responsibles and locations.

The human resources are described both in terms of

roles and instance performers, grouped by

departments or organisational units depicted as visual

containers, e.g.: Production; Research/Development;

Marketing; Finance; Resources. Figure 8 depicts

examples of models developed so far in the

implemented modelling tool: Model of make-to-order

process (M1); Model of participants (M2); Model of

locations (M3).

The first model contains a business process

comprising three types of tasks and also decisions.

The second type of diagram contains some

departments of the transport enterprise (in which we

have the employees) and also some business partners.

Finally, the third model contains cities or regions with

two types of parkings: small parking area and big

parking area. All these types of models can be linked

across

the ZF facets they represent: (i) the tasks are

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686

Figure 8: Samples of models in the multi-view modelling tool.

Figure 9: RDF model serialisation sample.

performed by employees; (ii) the tasks require

parking areas in cities.

In Figure 9 some fragments in RDF are presented,

describing the link between the Deliver task and the

Jim instance (including some positioning properties

relevant in case serialised models are imported in

another tool).

Based on these statements, we can run SPARQL

queries from any programming environment, in order

to access information from client applications (e.g., a

workflow management system). Some examples of

SPARQL queries are:

SELECT ?x WHERE

{?x a :Employee.}

SELECT ?role (Count(?task) as ?count)

WHERE {

?task a :DeliveryTask.

?task :AssignedRole ?role.}

The first query returns all the employees that work in

the given enterprise and the second query counts the

tasks assigned to a role. Based on a similar example,

paper (Buchmann et al., 2018) discussed the

Repurposing Zachman Framework Principles for "Enterprise Model"-Driven Engineering

687

overarching software engineering method enabled by

our proposal, whereas (Karagiannis and Buchmann,

2018) discusses the OWL reasoning opportunities

that are open by this proposal when using a graph

database management system such as GraphDB

(Ontotext, 2017).

6 CONCLUDING SWOT

ANALYSIS

The paper repurposes ZF principles to create a new

kind of enabler for enterprise model-driven

engineering – agile modelling tools that expose ZF

abstractions and their semantic links to knowledge-

driven software development processes. We

encapsulate the concluding discussion in a SWOT

analysis:

Strengths: The proposal provides a use case for

both (i) graph databases and (ii) agile modelling

methods which can deviate from standards in order to

fulfil enterprise-specific requirements. The ZF

abstractions are connected in ways that can be

exposed as RDF graphs and can be further

complemented by ontologies with the goal of deriving

hybrid knowledge bases – an aspect detailed in

(Karagiannis and Buchmann, 2018).

Weaknesses: Evaluations are necessary for

industry-oriented or standard languages; a client-side

proof-of-concept of model-aware application

benefitting from the proposed knowledge acquisition

enabler is not discussed in this paper – details on the

software engineering method that is enabled by the

proposal are available in (Buchmann et al., 2018).

Opportunities: (i) The GeoSPARQL ontology

(OGC, 2017) creates opportunities for geospatial

inferences, which are considered for the future

evolution of the tool; (ii) An enterprise should be

modelled in a multi-perspective manner and the

relations between those perspectives can be managed

by combining ZF's prescriptive schema, AMME and

RDF; (iii) Graph-like structures are more intuitive for

humans as means of knowledge representation and

the proposed tool could be evolved towards a

graphical RDF editor.

Threats: So far the adoption of RDF and

Semantic Web principles is still slow in enterprise

applications.

ACKNOWLEDGEMENT

This work is supported by the Romanian National

Research Authority through UEFISCDI, under grant

agreement PN-III-P2-2.1-PED-2016-1140.

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