A Systemic, Ontology-driven Approach to e-Services
Bill Karakostas
and Yannis Zorgios
Centre for HCI Design, School of Informatics, City University
London EC1V 0HB, U.K.
CLMS (UK) Ltd, 73 Park Lane, Croydon, CR0 1JG, U.K.
Abstract. This paper proposes an ontology driven, systemic approach to e-
services governance. E-service governance refers to frameworks and policies
for controlling the development and provision of e-services within an
organisation. Given the level of complexity of current e-services, it is necessary
to think of them as systems of interconnected elements that are more complex
than the sum of their parts. Our IDEF0 based, system theory inspired,
modelling approach, captures the essence of governing systems of e-services
contained (recursively) within higher order systems. We use ontologies to
represent explicitly systemic properties of services such as context,
control/constraints and feedback. Governance rules constrain the syntactical,
semantic, and behavioural properties of service ontologies and, due to the
hierarchical ordering of service systems, can be applied to e-service portfolio
management, architectural design compliance, and runtime SLA enforcement.
Ontology mapping capabilities allow governance rules to be described using
concepts appropriate for the different levels of service.
1 Introduction
SOA governance is an extension of IT governance that focuses on the life cycle of
services and composite applications in an organization’s Service Oriented
Architecture (SOA) [6] The purpose of SOA governance is to establish and maintain
the essential relations between the controlled services system and the larger
(controlling) organisational system [4].
According to [12] governance policies can affect every aspect of the service
lifecycle, including design, deployment, and operation. Governance can therefore be
classified into the following categories:
Service Identification Governance, driven by organisational considerations, market
analysis, stakeholders, policies, objectives etc.
Design Governance, i.e. rules and constraints to drive top down decomposition of
Runtime Governance that controls service execution via business environment
oriented feedback
Asset Governance, i.e. organisation policies for reuse of services assets
Karakostas B. and Zorgios Y. (2008).
A Systemic, Ontology-driven Approach to e-Services Governance.
In Proceedings of the 2nd International Workshop on Architectures, Concepts and Technologies for Service Oriented Computing, pages 41-51
DOI: 10.5220/0001884100410051
Management governance that refers to policies for adaptation, modification,
retirement, etc. of services
According to [4] the main benefits of e-services governance are:
Alignment of e-services to business needs.
Adaptability and integrity of e-services.
Ability of IT services and organisation systems to co-evolve effectively.
Enhancement of the business value of services.
From a service lifecycle perspective, governance rules fall into one of the following
three categories:
Portfolio Governance Rules: These refer to business modelling for SOA. They are
used to maintain associations between services and other asset types of the
Architectural Governance Rules: These rules ensure that services have been defined
according to the enterprise's SOA infrastructure. For example: does a service has a
higher level (business service) that defines its scope?
Behavioural Governance Rules: These rules constrain the runtime behaviour of a
service. These rules are used to enforce Quality of Service (QoS) and Service Level
(SLA) agreements at runtime.
This paper presents an approach to e-service governance that is influenced by
Systems theory as well as more recent work in using ontologies, to describe properties
of e-services. The paper is organised as follows. The next section discusses e-service
governance from a system theoretic perspective. Section 3 introduces the IDEF0
notation that allows the description of hierarchical systems of e-services. Section 4
formalises the concept of governance using hierarchical systems of ontological
descriptions. In Section 5 the concept of ontology based governance is illustrated for
different phases of service engineering, and with different types of ontologies. Section
6 outlines an approach to runtime governance using the concept of feedback. Finally,
section 7 discusses related work and suggests future research activities.
2 Systemic Principles of e-Service Governance
Systems theory, pioneered by von Bertalanffy, has been widely used as a framework
for understanding the nature of computer systems. In its origins, systems theory was
motivated by the desire to find a way to consider entities not as isolated beings but
related to their environment, yet maintaining a state that is not wholly at the mercy of
the environment, and so maintaining integrity as a distinct organism.
Systems theory views a system as having a boundary separating it from the
environment and across which interactions occur, and within the boundary are the
subsystems of which it is composed. Central to systems theory, is the concept of
hierarchical combination of sub-systems into systems, and their interaction with a
wider system that is the environment. Under this view, the environment may then be
seen as a wider system of which the system is a subsystem. By recursive application,
this rule yields a part-whole hierarchy. In the computer science domain, systems
theory depicts a computer system as composed of subsystems, sub-subsystems, and so
on. This provides considerable elegance in analysis, since the analyst may apply the
very same method of thinking to any layer in the hierarchy. However, an important
aspect in systems theory is that the hierarchy is composed of layers that demand
different levels of description [2]
Under the systemic perspective, services are hierarchical systems comprising
organisational (i.e. people, non IT systems, physical resources) and IT systems, that
automate in part or totally (i.e. as in the case of Web services) the service delivery. A
business service is a system of systems, as it comprises multiple heterogeneous,
distributed business and IT systems that are interlinked as networks at multiple levels
and in multiple domains. As services are provided by the manipulation of organisation
resources by processes, they are themselves systems embedded within larger systems
and ultimately within the organisation system. Thus, the principle of governance
applies in a recursive manner i.e. across the organisation, its services, and their
constituting parts.
Under this premise, service governance must be applied across several hierarchical
layers of business structures, IT architectures (e.g. SOA), IT systems, and
environments, down at the level of executable software services, for example Web
services (Table 1). Because, however, as argued above, different levels in hierarchical
systems typically require different levels of description, service governance may need
to be applied differently at each level. But, since service governance is essentially
about top down control, a fragmented and disjointed approach to governance will fail
to bring benefits such as alignment of e-services to business needs and co-evolution of
business and IT services.
Thus, the paper proposes a holistic approach to service governance that is driven
by a hierarchical systemic view of services.
Table 1. e-Service hierarchy levels.
The organisation context
The business system level
The SOA level
The Web service level
The technological infrastructure
The conceptual tool to implement this approach is provided by the IDEF0 systems
modelling methodology [5]. In particular, we exploit two principles of IDEF0:
the hierarchical decomposition of systems, and
the concept of control which can capture the semantics of governance rules
We employ the above modelling principles to apply governance rules at different
levels/systems, and to enforce their consistent application across the service systems
hierarchy. However, as argued by [12], to achieve consistency and automated
conformance checking, dynamic discovery binding and enforcement, such
governance rules should be in a machine-usable format .We additionally propose,
therefore, that ontologies can be used to describe governance rules in a machine
interpretable format. As different levels of the service hierarchy require different
representations, we employ multiple ontologies and ontology mapping mechanisms.
Indeed, this is the approach explored in the following sections of this paper.
3 The IDEF0 System Modelling Methodology
The IDEF0 system modelling notation [5] has been employed in systems analysis to
model systems of any type, as hierarchical ordered networks of atomic elements
called processes (and also functions or activities). IDEF0 introduces the concept of
‘control’ in a system model. The control depicted by the top entering arrow in a
‘process box’ is used to describe how the performance of the process is constrained
by specific rules, guidelines, or conditions that are required to produce the correct
output (Figure 1).
Fig. 1. IDEF0 notation.
The principle of hierarchical decomposition in IDEF0 suggests that a process can
be decomposed into a network of processes (typically 5-7). This decomposition can
be carried out over several levels (Figure 2).
Processes, at any level of decomposition, may have controls that are inherited from
the parent process, in addition to any new ones introduced at that level. IDEF,
though, has not formalised the concept of control, defining controls simply as
conditions required for the function to produce correct outputs. Intuitively, the
concept of control inheritance means that the conditions required for the correct
operation of a function must also apply to the correct operation of its children
In the context of service governance, the concept of control is suitable to be used
for representing governance rules. Thus, informally, each service concept must be
controlled by at least one governance rule. That rule specifies conformance conditions
for the service. For such conditions to be meaningful, however, they must refer to
system variables that can be monitored and measured, at that particular level of
decomposition, i.e. to service constructs, such as inputs, outputs, mechanisms (i.e.
resources) and their inter-relationships.
Of course, when a control (governance) rule is inherited from a higher level, it is
possible that the rule is originally expressed using concepts applicable to that (higher)
level. Therefore, because the rule might refer to the state of affairs of a different
universe of discourse, it may not be possible to test its validity in the current context.
We may need therefore to resort to model mapping/transformation techniques in order
to translate governance rules for each level of service system decomposition.
These ideas are formalised in the following section.
Top down propagation of
Fig. 2. Hierarchical decomposition of services.
4 Formalising Governance using Ontologies
As argued above, to achieve automated governance conformance checking, both
governance rules and service models must be expressed in a machine interpretable
notation. Ontologies provide us with this ability, i.e. with machine interpretable
models of a of discourse (domain). Here we define a service governance ontology O
as a tuple O := (C,R, H,G) where
C are service concepts,
R are intra-level concept relationships
H are hierarchy decomposition (inter-level) relationships that specifying how
service elements are decomposed (refined) over a succession of levels, and
G is the set of governance rules.
Fig. 3. Inheritance of controls.
A governance rule g
G (defined at level n of the hierarchy) is a logical relation of a
set of concepts c
where c
C and R
Assume that at decomposition level m (where m > n) the set of concepts c
used to describe the state of affairs, and that m inherits g
from level n. To be able to
test if rule g
is satisfied at level m, we need to infer all rules G’
from g
where H
is a subset of H, i.e. a set of relationships relating concepts belonging to c
, c
, ..., c
and then check all rules g
As an example, consider a governance rule, defined at level n, stating that for all
process p, the mechanism ƒ used to implement p must have been certified for quality
assurance. Assuming also a ‘realises’ type of relation, i.e. a partial mapping from m to
ƒ,p: Implements(ƒ, p) certified(ƒ).
p': realises (p', p)
ƒ’: implements(ƒ ', p')
infer rule
The above inference states that the mechanism ƒ' used to implement process p’
must be certified. Of course, the concept of certification may have different meanings,
depending on the level of the service hierarchy (i.e. business process certification vs.
software process certification). This governance rule can be enforced at the (SOA)
design phase. Other governance rules applying to the runtime phase are discussed in
subsequent sections.
5 Applying Ontologies to the Governance of e-Services
Ontologies, therefore, can be used to represent formalised governance knowledge, and
to actively assist in consistency checking, monitoring, and enforcement. Because of
Controls can be inherited from higher levels
our hierarchical systemic view of services, ontologies can be used to express
governance at the following phases:
Service Identification and Definition Phase. Ontologies at this phase represent
organisational goals and policies, resources and other critical assets. Governance
rules express fundamental axioms relating services to higher level business
concepts, such as, for example, that each service must be associated with at least
one organisational goal or objective.
Service Design phase, where method rules are applied to drive top-down
decomposition of services. Resource ontologies can be used to state such rules.
These can be embedded in a service design tool, integrated with an ontology editor,
to automatically check decomposition/inheritance rules in service design.
Service Lifecycle Phase. Ontologies here are used to express rules that govern and
enforce lifecycle policies.
Service Asset Management Phase. Ontologies here can be used to codify policies
for management (cataloguing, reusing, retiring) elementary/atomic services.
Service Runtime. Ontologies at this phase are used for monitoring and service
execution control. These can be embedded in the service runtime execution
environments. Constraint satisfaction systems, or theorem provers, can be used to
enforce for example SLA constraints on executable services At runtime, all
governance rules that are directly applied to the executing service or inherited from
ancestor levels are evaluated.
Ontology Mapping and Interoperability
When moving across hierarchy levels, we might need to translate between ontologies
syntactically or semantically. Ontologies might also need to be interoperable, for
example, a personal ontology must interoperate with a domain ontology, as in Table
In general, mapping between ontologies at different levels of the IDEF hierarchy
can be achieved in several possible ways i.e.
Via inheritance of concepts
Via integration
Via concept mapping. Ontology Mapping is the process whereby two ontologies
are semantically related at conceptual level, and the source ontology instances are
transformed into the target ontology entities according to those semantic relations.
In this approach we employ the concept mapping approach, where concepts from
different levels of the hierarchy are related with hierarchical 'implements' type of
relationships. This is not an 'is-a' (taxonomy) type of relationship as 'is-a' does not
capture the semantics of decomposition. For example, a Web service (process) in an
IDEF0 model is not a business or IT process at a higher level diagram; it is an
implementation of such process.
The following Table 2 summarises the above points.
Table 2. Ontological mappings for service governance.
Purpose of
Example ontology
Possible type of
Capture of
ontologies, goals,
missions to drive
ction of suitable
Enterprise ontology
repositories to
Guide the design
process, ensure
services are
obtain declarative
specs of web
The IDEF0 meta-model
[5], Process ontologies,
such as the Open
Source Business
Management Ontology
Personal service
ontologies, e.g. [7]
Integrated service
(IDEs) and
Check the
runtime services
for compliance
with SLA rules
and QoS
Semantic service
ontologies and
Rules e.g. [9].
monitoring and
'service bus'
6 Runtime Governance
A service oriented system, at runtime, is connected to its operational environment via
multiple feedback mechanisms. For effective alignment with the environment,
runtime e-services governance requires a mechanism for reasoning about continuous
changing conditions that affect running services, and impact governance rules and
constraints. Additionally, corrective actions may be required to bring the system back
within the permitted operational boundaries.
For e-services governance within the continuous changing environmental
conditions, it is, therefore, necessary to use a knowledge representation scheme
capable of representing the structural as well as the behavioural semantics of e-
services through governance rules that interpret feedback from the environment,
identify discrepancies between expected and apparent states and resolve conflict.
To accomplish this, first governance rules need to describe the discrepancy
between expected and received feedback and then link this with e-service variables
defined in terms of an e-services ontology.
For this purpose, we propose a runtime governance environment that provides
mechanisms for (a) alerting, in the light of feedback, regarding the behaviour of
changing conditions at a given time, and (b) represent and use feedback in order to
reason about the execution of governance rules to bring the system back to its
permitted state.
A reasoning engine (RE) has been developed to address the issues above. It
provides a rule based language capable of representing the interdependencies amongst
service variables, expressed in terms of ontologies, and to support reasoning about
continuous changing conditions within the scope of e-service governance.
Discrepancy Detection
The principal of conflict detection is based on detecting possible inconsistencies
between the feedback and the expected value that indicate potential violation of
governance rules. In order to carry out this approach, all governance rules in the
system are searched for possible discrepancies. In other words, discrepancy detection
is solely concerned with detecting inconsistencies between the values of e-service
variables as defined in the governance rules and the values received from the
environment. When a rule is found to be inconsistent, it is recorded as a failed rule.
The discrepancy detection algorithm is defined as follows:
For each feedback value x received
let v be the ontology variable whose x is its actual
for each Governance Rule R that contains v
if x == v.expected
R.status = “passed”
R.status = “failed”
In the above algorithm, the attribute x refers to the value of an e-services variable,
received as feedback from the environment. This could represent, for example, the
roundtrip time between a service request and a service response. SLAs might be in
place to constrain the permitted values for this response, in terms, for example, of
average, maximum or minimum times etc. The expected values of these variables are
described in the associated e-service ontology.
7 Discussion and Conclusions
SOA Governance has recently been proposed as an extension of IT Governance
concept to e-services. SOA Governance models have been proposed by [4], [11], and
others. In addition, the use of ontologies to describe services has been proposed both
in the context of organisation level services [1], consumer level services [7], and Web
services [9]. Ontologies have also been employed to assist with the specification of
quality standards for services [8].
The approach presented in this paper emphasises the hierarchical nature of services
governance. Because of their systemic aspect, service governance specification
approaches need to allow us to move freely across levels of e-service definitions,
from organisational to executable software, from service identification time to
execution and monitoring time. Not all current SOA standards, however, have
inherent support for hierarchical governance. In contrast, the IDEF0 modelling
formalism we have adopted has an inherent capability to capture the semantics of
governance in hierarchical service systems.
The key benefits of the proposed approach can be summarised as:
Semi-automated support to propagate governance rules through the IDEF
Achieved with the use of semi-automatic ontology mappings
Consistency checking of Governance rules through the service levels
Governance rules can be mapped to executable rules (e.g. in a scripting language,
database triggers, etc)
Our continuing research aims to build a totally automated service governance
environment that supports the concurrent specifications of e-services and governance
policies, translates governance policies using ontologies, and enforces them at
runtime using reasoning mechanisms (e.g. theorem provers and constraint satisfaction
systems). We hope that this work will eventually produce self diagnosing and
repairing e-service systems, making a step towards realising the vision of autonomic
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