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This is a pre-print of: Cybernetics and Systems: An International Journal, Volume 44, Issue 2–3, 2013, p. 245–263, DOI: 10.1080/01969722.2013.762274 Link to the article published by Taylor & Francis: http://dx.doi.org/10.1080/01969722.2013.762274 To cite the published article: Jarosław Pastuszak, Adam Czarnecki & Cezary Orłowski (2013): Ontology-Driven Rule-Based Model for an Extension of Information Technology Infrastructure Library Processes, Cybernetics and Systems: An International Journal, 44:2–3, 245–263

Ontology-Driven Rule-Based Model for an Extension of Information Technology Infrastructure Library Processes

Jarosław Pastuszak, Adam Czarnecki, Cezary Orłowski

Gdańsk University of Technology,

Faculty of Management and Economics,

Department of Applied Informatics,

ul. Narutowicza 11/12, 80-233 Gdańsk, Poland

jarek.pastuszak@gmail.com,

{Adam.Czarnecki, Cezary.Orlowski}@zie.pg.gda.pl

Abstract. The aim of this study is to present the stages for building a development model to create information

technology (IT) systems for IT service providers. In order to ensure the consistency of the model, a novel solution is

proposed where the stages of the model’s construction are controlled using ontologies dedicated to the ITIL

standard. In this article, a description of models used to assess the provider organization, with particular focus on the

ITIL model, is presented first. Then, the design stages of a custom graph and rule-based model of development are

introduced. Next, the role of ontologies in the assessment of the consistency of ITIL processes is explored. Further,

the resulting development model in its general and detailed versions is verified by means of its implementation.

Keywords: ITIL, IT service management, ontologies, rule-based model, SWRL

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INTRODUCTION

The issues associated with IT project management constitute a set of challenges faced by

software engineering managers/practitioners. The papers published so far usually address the

analysis of management aspects with particular attention to the management of project-oriented

organizations (i.e. IT-service providers) or IT aspects related to the use of IT technologies. In the

first case, sociological and psychological relations of organization members and their influence

on the success of a project are examined. The second case analyses the influence of IT

technologies on the state and development of the IT-service provider.

The state of an organization is analysed using ITIL version 3, in particular its two main

phases of implementation are considered. These are Service Transition (Lacy and Macfarlane

2007) and Service Operation (Cannon and Wheeldon 2007). The use of this standard in the

provider's organization is controversial because of its complexity and problems with its

implementation (England 2011). The controversy is also related to the static character of the

standard and the lack of any relation to the transition processes that occur during the execution of

an IT project. Thus, the state of an organization and its influence on the selection of the proper IT

technologies has become the subject of the research proposed herewith. As a result, a maturity

capsule model (Orłowski and Kowalczuk 2012) has been designed, where both psychological

and sociological relations as well as the status of the project, the provider’s organization, and the

customer are included. The capsule establishes a set of conditions for an assessment of the

project status, the dynamics of its changes, including the dynamics of changes to the customer's

organization, the provider, and the project's negentropy. The aim of this paper is to analyse one

of the components of the maturity capsule, that is, the state of the provider's organization and

changes that occur in the provider's organization during the execution of IT projects.

The process of extending the capabilities of the ITIL standard and the analysis of the

maturity capsule components indicates the consistency of ITIL processes as a challenge to be

explored. Yet, an examination of the consistency of the maturity capsule components is required.

In this paper, the use of ontology is exploited to improve the consistency of the verification

process. The proposed approach uses the graph development process including the Event-Driven

Process Chain (EPC) notation (Mendling 2008) and the rule development model based on the

structure of a graph. The application of the graph- and rule-based model is dictated by the

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necessity to organize the processes of the extension of the ITIL standard and to generate data

adjusted to the requirements of an ontological description. For this purpose, the Protégé 4.2.0

environment with a set of defined ITIL classes, objects, transition processes, and features of

these elements is deployed. Reasoning tools, such as Hermit 1.3.6 and FaCT++ enable the

verification of the consistency level of both the extended ITIL description and the maturity

capsule.

MODELS FOR THE EXTENSION OF ITIL PROCESSES

Figure 1 illustrates a general model of changes in the provider's organization. This model

presents the modifications within the provider's organization based on a five-staged life-cycle of

a service and transition processes.

Fig. 1. A general model of changes within the provider's organization

The transition processes correspond to the necessary changes that occur in the

organization when it reaches the levels of its maturity and thus, lead to better adjustments to IT

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project constraints. In the first stage (cf. Fig. 1), the consistency of the processes implemented in

the organization with the ITIL process library is analysed. In the second stage, decisions are

made with regard to process changes when no such consistency exists. The estimation (i.e.

description of the state) is used as the foundation for introducing changes to the ITIL processes.

The organization state is described as a sum of implemented processes. In addition, the transition

processes and the states of these processes are determined by enablers and stoppers (Dumoulin

and Spalding 2011). Also, the technologies are selected. Stage four corresponds to the

application of the decision support system for recommending the proper transition processes.

The description of its models constitutes the following sections of this paper.

The detailed model of changes to the provider's organization (cf. Fig. 2) as an instance of

the general model (cf. Fig. 1) is based on the assessment of ITIL processes and their influence on

the level of the organization's maturity. It is divided into three submodels:

• Submodel 1: Implementation Model for Individual ITIL Processes.

• Submodel 2: Dependencies Model for ITIL Processes.

• Submodel 3: Capability Model for ITIL Processes.

Fig. 2 A detailed model of changes in the provider's organization

The first submodel, called the Implementation Model for Individual ITIL Processes,

describes the rules for the individual implementation of a single ITIL process within an

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organization. These rules are based on both the ITIL requirements (e.g. roles, responsibilities,

measures, sets of actions, service life-cycle), and the rules associated with the project

management approach (e.g. pilot implementation, phase approach, management decisions). It

can be seen that the ITIL Processes set depends on a given IT organization’s requirements. The

second submodel, called the Dependencies Model for ITIL Processes, describes the relationship

between ITIL processes (from the implementation perspective) such as, for example,

the sequence of their implementation, their dependencies with triggers (enablers, stoppers), or

their required prerequisites. The purpose of the third submodel, called the Capability Model for

ITIL Processes, is to illustrate the relationship between the process capability level and

the maturity level of the entire organization (a staged representation of processes development).

This submodel could be used, for example, to develop the strategic goals of an IT organization’s

evolution.

These three submodels have been presented and analysed in previous papers (Pastuszak

and Orłowski n.d., Pastuszak et al. 2010, 2012, Stolarek et al. 2010). They have also been

described using (1) IF-THEN type rules, which are selected to represent the knowledge about the

organization and (2) Event-Driven Process Chain graphs, which are selected for the graphical

modelling of the rules. The decision to use a hybrid (rule-object) description was made because

of the problems with formal representations of knowledge from ITIL libraries.

The following model elements were defined during the implementation of knowledge:

Organization State (OS), that is, a formal triple of (1) the set of implemented processes (in

a formal or informal manner), operating procedures or similar activities with (2) general

organizational rules, regulations or policies, and (3) the organizational structure. Also, the

following Transition Processes (TP) constitute model elements, such as a program, a project, or

a set of activities similar in nature having a significant impact on the Organization State. An

enabler, that is, a single or continuous fact necessary to initiate a TP counts as a model element

as well. A stopper, that is, a single or continuous fact that prevents the completion of the TP (and

causes an organization to remain in its previous state) also belongs to the model elements pool.

Based on the model elements, variables and their values were determined. Figure 3 shows

a decomposition of these elements to linguistic values. The decomposition process is based on

the system elements, for which linguistic variables have been generated with specific values

assigned.

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Fig. 3 A model for the decomposition of model elements to linguistic values

Table 1 lists selected elements of the detailed model, their variables, and the

corresponding linguistic values (cf. Fig. 2).

Table 1. List of selected variables of the model elements

Transition Process variables Organization State Variables

Symbol Variable Name Symbol Variable Name

TP2 Establishment of service awareness OS2 IT Department Structure

TP3 Establishment of process awareness OS3 Service Awareness

TP4 Organizational change OS9 Process implemented Enabler variables / Stopper variables Symbol Variable Name Symbol Variable Name

E1 Authorization of ITIL Process Implementation E6 Authorization of a change of IT

Department Structure

E2 Pilot implementation accepted S1 Pilot implementation rejected

E3 Authorization of Subprocess Implementation S2 Process freezing

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Table 1 includes three TP variables defined as TP2, TP3, TP4, three Organization State

variables (OS2, OS3, OS9) and four variables for enablers (E1, E2, E3, E6), and two variables

for stoppers (S1, S2). The EPC graph notation was used for designing the linguistic description.

The use of this approach improved the development of the rule-based description and proved to

be clear to those who were developing the model and to ITIL experts. Figure 4 shows an

example of an EPC graph that presents the implementation of an ITIL process called Availability

Management.

The Availability Management process implementation can be briefly described as

follows. After the corresponding IT organization change and the positive authorization of the

Chief Information Officer (CIO), the role of the process owner is established and the process is

designed (i.e. process flaws, roles and responsibilities matrix, operational procedures, etc.).

Then, the pilot implementation starts (a process within an organization and simultaneously

a management tool). In the case of a positive assessment of the results of the pilot, the

implementation is continued and completed in two phases reflected by two subprocesses:

Component and Service Availability. In each case, the CIO's authorization is required. In the

case of the rejection of the pilot, the designing phase is repeated. See below a set of the first three

rules (out of eight) that describe this graph.

R1: IF OS2=Service Design AND E1=Availability Management AND

TP7=Availability Management THEN OS12=Availability

Management (1)

R2: IF OS12=Availability Management AND TP6=Availability

Management THEN OS6=Availability Management (2)

R3: IF OS6=Availability Management AND TP8=Availability

Management THEN OS7=Availability Management (3)

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OS2: IT Department Structure =

„Service Design”

E1: Authorization of ITIL Process Implementation =„Availability Management”

TP6: Process Design = „Availability

Management”

TP7: Process Owner Appointment =

„Availability Management”

OS6: Process Designed = „Availability

Management”

TP12: Complete Implementation of the Process Management

Tool = „Availability Management”

OS11: Process Management Tool

Implemented=„Availability

Management”

OS12: The Role of Process Owner Established =

„Availability Management”

TP8: Process Pilot Implementation =

„Availability Management”

OS7: Pilot Process Implementation

Completed=„Availability Management”

E2: Pilot Implementation

Accepted = „Availability

Management”

TP11: Pilot Implementation of

Process Management Tool = „Availability

Management”

OS10: Pilot Implementation of Process Management Tool

Completed=„Availability Management”

V

XOR

XOR

S1: Pilot Implementation

Rejected = „Availability

Management”

V

V

V

Evaluation of the Pilot Implementation of

„Availability Management”

TP9: Implementation of the Subprocess =

„Component Availability

Management”

E3: Authorization of Subprocess Implementation=

„Component Availability Management”

V

OS8: Implementation of subprocess completed = „Component Availability

Management”

TP9: Implementation of the Subprocess = „Service Availability

Management”

E3: Authorization of Subprocess Implementation=

„Service Availability Management”

V

OS8: Implementation of subprocess completed =

„Service Availability Management”

OS9: Process Implemented=

„Availability Management”

V

Fig. 4. The EPC graph showing the implementation of the Availability Management process

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ONTOLOGICAL VERIFICATION OF A DETAILED MODEL

After the development of a detailed model and designing its rule and graph representation,

ontological and replicative verification in an organization using the ITIL standard was

performed. The purpose of using ontology for verification was to analyse the consistency of the

graphs and rules of the detailed model. The method of knowledge representation in the form of

an ontology based on description logic (Allemang and Hendler 2011) enables the representation

of ITIL domain semantics and the extension of this domain (i.e. the rules for the implementation

of best management practices for IT services). Such an approach to knowledge representation,

especially regarding information technology standards and projects, was introduced in earlier

works (Czarnecki and Orłowski 2007, 2008, 2010a, 2010b, 2011a, 2011b, Sanin et al. 2007,

Czarnecki et al. 2008, Orłowski et al. 2010).

The first element to be analysed was the consistency of the notion for ITIL processes.

Then, the process structure for the activities found on levels I and II was explored. Thus, the

comparison and assessment of processes implemented by the organization with the processes

defined in the best practices documentation has been defined. The authors of this paper did not

succeed in finding an ITIL ontology which could be used as a “terminology box”. Because of

this, a decision was made to create such an ontology based on the documentation of the so-called

ITIL Core (Case and Spalding 2007; Lloyd and Rudd 2007; Cannon and Wheeldon 2007; Iqbal

and Nieves 2007; Lacy and Macfarlane 2007).

Since the comparison within the model being described applies to the ITIL processes, the

ontology developed included key terms related to these processes. These are mainly the process

names (ITIL_Process, 27 classes), the names of roles (ITIL_People, 49 classes) or functions

(ITIL_Function, 4 main classes). There are also stages in accordance with their descriptions in

applicable publications (ITIL_Phase, 5 classes). The structure of the main classes based on ITIL

documents is presented in Fig. 5.

Next, the ontology of the process structure from submodels 1 and 2 (cf. Fig. 2) was built.

It enabled the course of activities to be shown pursuant to ITIL and the comparison with the

processes implemented within the organization (levels I and II of the general model). Due to the

fact that graphs are used to present processes in the ITIL documentation, it is also justified to add

to the model a metaontology that defines (or at least enumerates) the elements of this type of

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charts (23 classes of concepts). The last stage of ontological verification was the verification of

rules. First, an ontology including the rules for extending ITIL with implementation and

efficiency aspects was developed (as a “rapid prototype”). These aspects are analyzed during

level III of the general model being described (cf. Fig. 1). As a more comprehensive reflection of

practices described in ITIL was not the aim of this stage of work, the scope of ontology was

limited to the area necessary for the development of the rules.

First-class citizens, such as classes, object properties, and individuals were used for the

verification of the process structure (see the description of selected elements in Table 2). Apart

from the listed types of concepts, the ontology also includes defining elements that enable

the automatic classification of specific terms. Fig. 6 is a graphic representation of the enumerated

classes. “≡” refers to the classes with previously declared constraints associated with their

equivalence (i.e. necessary conditions). The details of the accepted naming convention for

variables and values can be found in (Pastuszak et al. 2012).

Fig. 5. Main categories associated with ITIL

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Table 2. Selected elements of the ontology of ITIL rules

Element Type Description

Organization_State class a set of individuals that symbolize organization states

OS0..OS13 individual variables for describing organization states

OS0a..OS13c individual variable values describing organization states

Value class a set of values defined by restrictions; includes subdomain subclasses

Variable class a set of variables defined by restrictions; includes subdomain subclasses

hasAllowedValue element value

links each of the variables with the values it can take (e.g. TP1 hasAllowedValue TP1a); also used for applying existence restrictions to a class which groups a set of values, i.e. Value (∃ hasAllowedValue Thing).

hasValue element value

for assigning specific values to individuals that represent variables; used within the ontology of ITIL rules for defining rules and to describe the analysed organization

Fig. 6. Relations between classes in the ontology of the ITIL implementation rules

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The semantics used in the ontology enabled a classification of individuals to be defined

that refer to variables and values. Classes that refer to variable and value categories were

distinguished. Further, the concluding technical means have shown the formal consistency of the

ontology. Semantic Web Rule Language (SWRL) was used for this purpose. SWRL is

a combination of the OWL DL/Lite and Unary/Binary Datalog RuleML (Horrocks et al. 2004)

languages. Because of the challenges related to decidability in SWRL, “safe rules” were defined.

They were constituted by the variables included in the premises of a rule and are atomic elements

that do not require the use of description logic. For example, the R1 rule presented earlier in this

paper uses the following SWRL notation (as per Protégé editor):

R4: hasValue(OS2, OS2d), hasValue(E1, E1j), hasValue(TP7, TP7f) -> hasValue(OS12, OS12f)

(4)

The premises in a rule may also lead to more than one conclusion:

R5: hasValue(E1, E1d), hasValue(OS2, OS2c) -> hasValue(TP6, TP6b), hasValue(TP7, TP7b)

(5)

The concept and process correctness examination established conditions for the analysis

of rule-based description correctness. Operational and tactical rules were developed during the

first iteration of rule development using SWRL.

A set of experiments was developed and conducted for this purpose. One of the

experiments was based on a fragment of an EPC graph which showed the implementation of

Availability Management (cf. Fig. 4) described by the rules R1, R2 and R3, presented earlier in

this paper. It consisted of the introduction of knowledge about the status of ITIL implementation

in the organization and then the derivation of a conclusion using an inference engine. There was

an additional value in the selection of rules R1, R2, and R3. They form a chain in such a manner

that the conclusion of R1 is a component of the premise in R2 and the conclusion of R2 is found

in the premise for R3. Hence, the verification of conclusion correctness for each of the rules was

possible. In addition, it allowed the checking of whether the engine that was used for drawing

conclusions from ontology is capable of exploiting the subsequent rules.

For verification purposes, eight individuals were created within the Case1 class. Their

aim was to reflect on the knowledge about a specific organization in terms of its states, enablers,

and transition processes (called Case1_Param1..Case1_Param8) according to the ITIL model of

process changes. In order to attain this goal:

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• each of these individuals is characterized by a sameness property assigned to an individual

symbolizing a variable from the ITIL rule model (OWL SameIndividual construction),

• the individuals located in the conditional part of the rules were associated to the individuals

that symbolize variable values (in a 1–1 relation) using the hasValue role; the individuals

describing those variables that were associated with a non-deduced value were left out of the

scope.

The expected effect of an inference engine should be the assignment of values to

variables (symbolic, using individuals and the hasValue relation) and the use of effects: of rule

R1 in rule R2 and of rule R2 in rule R3. The authors used two inference engines conforming to

the so-called safe rules of SWRL/SWRL-DL: HermiT and Pellet. The FaCT++ reasoner,

available also in Protégé, does not support rules and returned incorrect results. Both tools

generated similar conclusions for the three rules that were analyzed. This means that it is

possible to automatically transfer the derived knowledge to subsequent rules. Figure 7 shows the

effect of reasoning for the conclusion of rule R3 (shaded items): identification with an individual

OS7 in the left panel (Description). The value hasValue OS7f in the right panel (Property

assertions) means that the variable OS7 (Completed pilot process) has taken the value of OS7f

(Availability Management).

Fig. 7. Description of an individual symbolizing the R3 conclusion, including the concluding

results

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In practice, two individuals referring to the conclusions of R1 and R2 rules could have

been left out in the data used for the simulation of knowledge related to a specific organization.

This is because it would have been possible to make the conclusion of rule R3 without them.

They were used in the experiment only to check whether the intermediate conclusions are

correct. Rules associated with process efficiency and organization maturity will be added in the

further stage of research because of their higher complexity.

Unfortunately, the selected method is not flawless. The use of the same identity

semantics (SameIndividual) means that knowledge about one organization only can be present in

an ontology at any one time. Otherwise, assertions about these organizations can interpenetrate

in an undesirable manner. In such a situation, it is impossible to use individual separation

semantics because it would lead to the inconsistency of the classification when using the same

variables. This is a side-effect of the rapid approach to the creation of an ontological ITIL rule

model. An attempt will be made to eliminate this during the next iteration of the research, which

is currently underway.

Despite the remarks in the previous paragraph, the above-mentioned results mean that the

expectation for the ontology to enable answering the no. 4 competency question is met. The

experiment proved that the ontology of ITIL rules at its present stage of development has enough

capabilities to provide the knowledge required by the competency questions nos. 1–4:

1. What variables are included in the rule model?

2. What values can each of the variables take?

3. What are the premises and conclusions of each rule?

4. What is the result of drawing conclusions for a specific organization based on the received

knowledge set about that organization?

5. What types of rules/submodels were used for processing the supplied knowledge about that

organization?

Questions from 1 to 3 refer directly to the knowledge asserted in the ontology. The

answers to questions from 4 to 5 are provided by an inference engine operating on

RDF/RDFS/OWL/SWRL expressions. As a result, the main part of the ontology includes

concepts that correspond to entities of general and detailed models described earlier in this paper.

These are:

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• Variables of the general and detailed model,

• Values which can be taken by any of the variables,

• Relations, mainly the ones reflecting the dependencies between variables and values,

• Rules of the detailed model.

VERIFICATION OF THE DETAILED MODEL IN AN ORGANIZATION USING THE

ITIL STANDARD

A replicative verification of models (following the ontological verification) was based on a case

study. The case study comes from one of the authors' parent organization. The study took place

in November 2012 in a 500+ employees IT support organization of one of the medium-sized

universal banks in Poland. Figure 8 shows a diagram for the verification of models.

Fig. 8. A general verification model of the detailed model

The verification process provided in Fig. 8 included a model analysis, a case study, and

a fact analysis (development forecasts) generated by the model and the facts that occurred during

the development of the bank organization. The fact list generated by the model had a varied

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structure depending on submodels 1 and 2. Submodel 3 was not verified. Below, a description of

example facts generated by the detailed model is provided. For submodel 1 (cf. Fig. 2):

(ITIL_Process_Name;

Deliverable_of_Process_Implementation_Project OR

(Deliverable_of_Process_Implementation_Project AND Trigger)

OR Trigger; After OR In_Parallel;

Deliverable_of_Process_Implementation_Project).

(6)

And for submodel 2 (cf. Fig. 2):

(ITIL_Process_Name OR Trigger; After OR In_Parallel OR

Independent; ITIL_Process_Name OR Trigger). (7)

The case study was performed in the form of interviews with people responsible for

process management in IT support organizations. Note that the detailed model was not presented

during the interviews. Each interview comprised the following parts:

• A short description of the analyzed organization, interviewees, the date,

• Implemented ITIL processes,

• The order of implementation of the ITIL processes within the organization,

• ITIL processes to be implemented,

• The planned order of implementation of new ITIL processes,

• The rules for implementation of ITIL processes (implementation phases, products,

management decisions, roles in a process, organizational changes, tool support),

• A subjective assessment of the implementations (i.e. the process met the assumed goals,

the process will be further developed, the influence of the organization's maturity, the

most important lessons learned).

In the case of significant differences in the forecasts, the conclusions of the detailed

model's rules were modified. The number of modifications throughout the experiment was

insignificant.

The interviewees who took part in the analysis had the following functions: Operational

Processes Management Office Manager and IT Services Management Office Manager. The

following ITIL processes were analysed during the case study: Incident Management, Problem

Management, Request Fulfillment, Access Management, Change Management, Configuration

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Management, Service Level Management, Event Management, Availability Management,

Capacity Management, IT Service Continuity Management, Supplier Management, IT Financial

Management. The following processes have been scheduled for implementation: Proactive

Problem Management, Release and Deployment Management, Knowledge Management. It was

also planned to extend Service Level Management (SLM) with a customer experience process

and to link business processes with the bank products.

The following issues have been analysed in the experiment (based on the submodel 2):

• (Incident Management; After; establishing a Service Desk),

• (Incident Management; In_Parallel; Change Management),

• (Service Level Management — availability of applications; After; Incident

Management),

• (Problem Management — reactive; After; Incident Management),

• (Event Management; After, Incident Management),

• (Service Level Management — availability of applications; In_Parallel; Availability

Management),

• (Business Capacity Management; In_Parallel; Resource Capacity Management),

• (Supplier Management; After; SLM),

• For all ITIL Processes: (process implementation; After; CIO's decision to implement

the process),

• For all ITIL Processes: (implementation of a process within the organization; After;

development of a mechanism for process implementation in the organization).

The following issues have been analysed in the experiment (based on the submodel 1):

• For all ITIL Processes: (ITIL Process; process implementation; After; pilot

implementation of a Process),

• For all ITIL Processes: (ITIL Process; process modelling; After; establishing

a Process owner),

• (Capacity Management; Business Capacity Management; In_Parallel; Resource

Capacity Management).

The use of the model resulted in the following subjective assessments of the ITIL

processes that had been implemented:

• The implemented processes were further developed.

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• The organization maturity increased (verified by internal process maturity

assessments, etc.).

• With the exception of Problem Management, the implemented processes met their

goals.

• The organization’s limited capability to absorb (i.e. to accept and apply) changes –

big, significant changes should be implemented with a reasonable frequency, in the

form of big projects.

• Management by measurement proved to be useful for the management of an IT

support organization.

SUMMARY

The aim of this paper was to present the capabilities, appropriateness, and the results of using

ontology to verify graph and rule models. Such a verification becomes necessary when the

models for complex management processes are being developed. This paper shows a set of

construction stages to create an organization development model. The development processes

had been divided into stages by distinguishing relevant products, that is, a general and a detailed

model. The detailed model was subjected to ontological and replicative verification processes in

the actual provider's organization environment. The results from this study enabled the following

conclusions to be drawn:

• The development of the provider's organization model should be limited to operational

and tactical rules. This limitation results from the requirement to focus on the extension

of the number and semantics of these rules to enable conclusions to be drawn in the area

of the efficiency of ITIL processes and the IT organization maturity.

• Also, further activities for the broader ITIL best practices ontology are necessary. Taking

both issues into consideration, it appears that the activities should be directed at the

development of a rule ontology based on the general ITIL domain ontology. In such

cases, it would be possible to try to support each of the four levels of the general model

of ITIL process transformation with ontologies.

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• As the experiment illustrates, an ontological environment for the representation of

knowledge in the ITIL domain and the rules to extend it enables the correct conclusions

to be drawn from variables and their organization description value.

• However, the development of the general model described in the introduction section has

shown that the initial scope of the ontology should be extended. Thus, currently the

system exploits the knowledge from two independent ontologies (i.e. ITIL itself and the

rules of ITIL transition processes) that eventually should be combined.

• The ontology of ITIL transition processes has revealed a limitation for a number of

organizations whose descriptions may be placed in an “Assertional Box” (ABox) at

a time. In this light, further activities should be applied to remove this limitation. Using

an ontology for the domain described in the model appears arguable.

• The replicative feature of the model was confirmed in the verification analysis for both

submodels 1 and 2. A better match for submodel 1 was found but this could have been

caused by the amount of facts collected.

• The verification processes have shown that the analysed graph and rule model was far

more general than the collected facts. It was found that in the case of two- or more phase

ITIL implementations the arbitrary order of implementation selected for the model did

not reflect the reality (i.e. subprocesses are often implemented simultaneously or in

a different order).

• It was also found that the consistency for Problem Management was obtained only

because of the process owner’s lack of sufficiently high qualifications. The organization

was ready to implement two subprocesses simultaneously. This observation should

probably lead to the use of a more flexible phase approach in the subsequent versions of

the graph and rule model.

Acknowledgments. The authors wish to thank the Head of the IT Department of Bank BPH and

the Head of the Department of Applied Informatics in the Faculty of Management and

Economics at Gdansk University of Technology for partial support of this work. We are grateful

to all colleagues and students who contributed to this study.

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