=Paper=
{{Paper
|id=None
|storemode=property
|title=Organic Aggregation Service Engineering Framework (OASEF): A New Model-driven Approach to Service Engineering
|pdfUrl=https://ceur-ws.org/Vol-608/paper4.pdf
|volume=Vol-608
|dblpUrl=https://dblp.org/rec/conf/tools/Wang10
}}
==Organic Aggregation Service Engineering Framework (OASEF): A New Model-driven Approach to Service Engineering==
https://ceur-ws.org/Vol-608/paper4.pdf
OASEF: A Synthetic Approach to Service
Engineering
Yuanzhi Wang
Department of Computer Science
The Australian National University, Australia
derek.wang@anu.edu.au
Abstract. Engineering complex service-oriented systems presents grand
challenges due to their great complexity, volatility, and uncertainty in
their rapidly evolving technology and social contexts. It demands an ef-
fective engineering approach in order to satisfy the needs of service eco-
nomics. This paper proposes an approach called Organic Aggregation
Service Engineering Framework (OASEF). Experience from proof-of-
concepts case studies shows that it provides a practical means to develop
service-oriented systems. It enables and promotes a focus on higher-level
intellectual engineering efforts, and provides a mechanism to capture and
reuse engineering capacities in a model-driven environment.
Keywords: Service Engineering, Model-driven Engineering, Service-oriented
Computing
1 Introduction
Deriving and managing complex Service-Oriented Systems (SOS) presents great
challenges due to their nature and characteristics in a dynamically complex en-
vironments [1]. Firstly, SOS often involve great complexity in terms of variety,
scope, and inter-relationship. On the one hand, technologies such as Service-
Oriented Computing (SOC) and Cloud Computing (CC) greatly facilitate de-
velopment and integration of applications and systems. On the other hand, eco-
nomical and social globalisation processes inevitably force individuals, organi-
sations, and communities to collaborate and compete with each other within
interacting value-chains. As a result, the size, variety, and scope of interrelated
systems scales up rapidly.
Secondly, SOS often present a high degree of uncertainty. Autonomous ser-
vices are often provided and consumed by different agents for different types
of purposes, without necessarily knowing each other in advance [2]. Moreover,
defining a service contract at any time cannot completely incorporate unknown
usage with necessary variations. Therefore, it presents great challenges to sat-
isfy not only the explicit requirements of current target applications, but also
the needs of envisioned future applications or unknown potential users. Lastly,
in highly volatile and heterogeneous environments, the velocity and variety of
appropriate response have to match those of the environmental changes. An ef-
fective service engineering hence has to support and facilitate dynamic evolution
by changing and reshaping their internal structures and behaviour, in a more
frequent and predictable fashion, compared with traditional systems.
These special characteristics and challenges can hardly be resolved by merely
adopting traditional software processes and engineering methodologies, or solely
relying on more advanced implementation technologies [3]. In order to enable
incorporation of wide range of business and social systems within rapidly grow-
ing global service economics, there is an important and urgent demand for a
mature discipline of service engineering [4]. In our view, such a discipline, to
be effective, must provide sufficient support for addressing the grand challenges
of complexity, uncertainty, and volatility while engineering SOS in their open
environments. We believe one way to approximate this ideal is to enable and
promote derivation and reuse of important higher-order intellectual efforts and
lower level technological capacities. This is because evidences have shown that
great complexity involved in engineering problems is better dealt with by intel-
lectual cognitive experience and capacities, which lead to sensible perception of
reality, logical reasoning of problematic situations, and systematic derivation of
conceptual plans [5]. Therefore, effective and efficient accumulation and use of
these intellectual resources is the key crucial factor to manage the ever-presenting
complexity, uncertainty, and volatility in a timely fashion while changes present
themselves. In the meantime, in order to take full advantage of advanced tech-
nologies and implementation infrastructures, and at the same time, deal with
their increasing heterogeneity and complexity, a discipline of service engineering
should also exploit effective means to develop, encapsulate, and automate the
engineering capacities and processes about lower-level realisation and implemen-
tation.
Therefore, our vision of an effective service engineering approach should en-
able and support efficient and flexible formation and exploitation of both higher-
order intellectual resources and lower-order implementation processes. That is,
both types of engineering resources should be explicitly identified, developed,
captured, and used, as major engineering means, to produce and manage sys-
tems, in a flexible, systematic, and automatic means, at least partially if pro-
cessing in its entirety is not possible. Specifically, our objective is to achieve
an effective service engineering approach that: i) promotes a focus on high-level
intellectual activities within the world of human mind, such as exploring and un-
derstanding problematic situations, and identifying and capturing higher order
engineering purposes and intentions; ii) links the captured higher-order engineer-
ing resources with other lower-order engineering activities by using the former
to guide and shape the latter systematically and sensibly, within a coherent
overall process; and iii) provides a means to capture, aggregate, and reuse these
important engineering resources, including both higher-order and lower-order
capacities, to facilitate flexible and rapid aggregations of processes and systems.
This paper presents a new approach to service engineering based on such
a vision, which is called Organic Aggregation Service Engineering Framework
(OASEF). It is based on a philosophy that takes a synthetic approach to growing
and managing processes and systems via sensible and responsive aggregations of
resources and capacities. This work mainly has the following contribution: firstly,
it emphasises exploration and exploitation of higher-order engineering efforts,
and links them with lower-level technological resources within an overall process
model; secondly, it promotes a concept of organic aggregation in engineering that
captures engineering capacities as reusable resources, and, more importantly,
uses them in a sensible, agile, and controlled fashion.
The remainder of the paper is organised as follows: section 2 presents the pro-
posed OASEF framework in details, including a general engineering framework it
conforms to, its conceptual model, and modelling methodology; section 3 briefly
illustrates its important concepts and methods using results from two proof-of-
concept case studies; section 4 analyses its features and limitations based on
observation and experience during the case studies, and compares it with some
related work; section 5 concludes this paper with a summary.
2 The OASEF approach to service engineering
It is important for a discipline of service engineering to have a well-founded
engineering framework that coherently organises engineering activities and re-
sources according to the nature and characteristics of services and systems under
consideration. The aim of this work is to provide and assess such a framework.
2.1 OASEF conceptual model
The design of OASEF started from generalisation of other engineering disci-
plines. Figure 1 depicts a general conceptual framework to which OASEF con-
forms. It includes a theoretical foundation, or a coherent conceptual system,
which consists of of inter-related theories, knowledge, and wisdom that are
shaped or influenced by individual and social experience, beliefs, philosophies,
and culture [6]. Such a foundation, either implicitly or explicitly, guides and
shapes, positive experience and practices that are justified in the course of con-
tinuous engineering activities, which gradually form a range of concrete and
specific guiding principles. These principles, articulated, well explained, and un-
derstood, provide valuable guidance for engineering activities in practice.
Moreover, based on the abstract foundation and principles, more concrete
engineering activities are arranged and conducted within a specific engineer-
ing process, which, in turn, is realised or supported by a range of practical
engineering methodologies. The latter provides specific and concrete means to
realise the aiming higher level engineering purposes, and to solve practical hu-
man problems in a controllable, repeatable, and efficient fashion [6]. Specific
languages, tools, and implementation techniques practically enable or facilitate
the engineering processes and methodologies, and eventually produce or manage
the target systems in reality. Altogether, these coherent engineering elements,
Fig. 1: A meta-model for service engineering
involving a variety of stakeholders, contribute to engineering of resources, infras-
tructure, events, processes, and systems that aim to satisfy identified engineering
needs and human purposes.
The overall structure of OASEF is depicted in figure 2. It conforms to the
general framework, and is based on a theoretical foundation rooted in some
multi-disciplinary knowledge, such as general system theory and modern philos-
ophy of mind. It also incorporates some practical principles by which its pro-
cess and methodology are guided or reinforced. These guiding principles, such
as “environment-driven view of service” [7], “pervasive change and evolution”,
“support for systems adaptation and agility”, and “facilitating knowledge and
capability reuse”, will not be presented here due to space limitation. Although
their meanings are well known, widely accepted, and often taken for granted,
they provide important empirically verified guidance for engineering activities.
As shown in the figure, OASEF incorporates a flexible process model called
Organic Aggregation Process (OAP) in the context of service engineering. Fig-
ure 3 illustrates its inner structure and inter-relationships at a detailed level.
The lines with text besides them represent specific process activities, whereas
the joint point between two lines represents direct correspondence between one
activity to another. Moreover, activities are arranged hierarchically in this struc-
ture. An activity represented by a single line, starting from point A to point B
in the clockwise direction, may comprise a sequence of subordinate activities
that are represented by a series of adjacent lines, which also starts from point
A and ends with point B clockwise. Superordinate activities are represented by
thicker lines and larger-size fonts on the hierarchy. For example, perception com-
prises subordinate activities of sensation and abstraction. The latter, in turn,
comprises system abstraction and general abstraction.
The OAP concepts and process are integrated in OASEF at various level,
as illustrated in the middle part of figure 2. The activity of Perception acquires
information about the Reality world through Sensation, and forms general or
system-specific abstraction of knowledge. The latter is used by Conception to
conduct human intellectual activities in the world of Mind. Specifically, Concep-
Process (OAP) Reality World
Guiding principles Model−driven Method
Perception (Eclipse EMF/MOF/AOT)
Environment−driven view
Evolutionary view Data, Models, Documents...
General Abstraction
Adaptation & Agility Sensation general abstraction: causation network
Capacities reuse, pattern system abstraction:UML,BPMN...
System Abstraction
Feedback & Monitoring service,process: soaML,BPMN...
Competition&Cooperation Rationalisation World Images
Communication matters
Models of various forms
Problem Situation Desired End Problem situation models
Desired End models
Rational Images
Realisation
Service Modeling
Capability Exploration Capability Design
Service Model, soaML,BPMN...
Process Modeling
Style, Platform, Framework BPMN, UML
Capability Images
Action
Service Discovery Service Invocation Service Implementation
BPEL, JAVA...
Service Provision Service Composition
Service Infrasturcture Runtime environment
App Server, Service Engine...
Service Management
Service System Images
Foundation: System theory, theory of organism and evolution, complexity and chaos theory
theory of communicative action, philosophies in epistemology ...
Fig. 2: OASEF: Organic Aggregation Service Engineering Framework
tion activities involve Rationalisation, the reasoning and sense-making processes
that produce understanding of perceived situations, inferred problems, and de-
sired ends, which are realised by subordinate activities such as Problem Situa-
tion and Desired End. Conception also includes high-level design, engineering
decision-making, and plan-making sub-processes through Realisation activities,
such as Capability Exploration and Capability Design that identify and design
desired capabilities in accordance with outcome of Rationalisation. Furthermore
Action, conducted in the world of Reality, deal with concrete service systems
by finding and invoking existing services, providing new services, or composing
composite services. Service Infrastructure provides information of implementa-
tion environment such as Enterprise Service Bus (ESB), whereas Service Man-
agement provides control and monitoring of services in run-time environments.
Therefore, OASEF derives and manages services using interconnected OAP
activities. Achievement of these activities collectively forms specific images rep-
resenting unified repository of knowledge, which can be referenced, or used as
Fig. 3: The structure of OAP process model in service engineering
input, by successive activities. For example, the Rationalisation takes World
Images from Perception as input. It produces Rational Images that represent
rationalised Desired Ends models to deal with identified Problem Situation. Real-
isation takes the Rational Images and produces Capability Images that represent
required realisable capabilities. The latter is taken by Action as input and even-
tually produces SOS that alter the state of the Reality with an aim of improving
rationalised problematic situations.
2.2 OASEF modelling and tools support
The conceptual model of OASEF is brought into reality using specific modelling
techniques and model-driven methodologies. Various abstract models, either gen-
eral purpose modelling language such as UML, or specially designed modelling
languages, are used as essential artefacts to facilitate OASEF activities, and to
link these activities altogether throughout the engineering life cycle.
OASEF emphasises activities of Abstraction and Rationalisation in the early
stage of the life cycle. The objective of Rationalisation modelling is to analy-
sis and justify the rationale and needs of engineering processes toward sensible
decisions of actions. Two types of modelling formalism, namely Problem Situ-
ation Models (PSM) and Desired End Models (DEM), are designed to capture
the crucial higher order cognitive achievement. For example, output of Problem
Situation activity is captured in terms of complex inter-relationships between
various problems, facts and constraints, and high-level purposes. Together, PSM
and DEM provide an important means to explore and represent problematic sit-
uations and desired ends that justify successive engineering activities and their
produced engineering results.
As an example, the graphical notations of DEM are depicted in figure 4.
Coloured rectangles with names inside represent various desired ends elements.
They capture the engineering intentions in terms of their concreteness and scope.
For example a Goal is more specific in scope, less abstract to define and commu-
nicate, and easier to measure, compared with Objective and Ideal. Specifically,
light yellow rectangles represent Ideal, whereas light green and dark green ones
represent Objective and Goal respectively. Moreover, white rectangles are used to
represent higher order Capabilities that provide contextual meaning and desired
value in support of service identification and realisation. Relationships among
various DEM elements are represented by connecting arrowed lines. For exam-
ple, an arrowed line can link a Ideal with its subordinate Objective, a Objective
with composing Goal, or a Goal with its subordinate one.
Fig. 4: Graphic notations of DEM
Every OASEF activities produce either domain-specific or general-purpose
models that all conform to a unified meta-meta-models. Therefore, activities in
OASEF can be conducted and inter-connected in a coherent fashion by manip-
ulating and utilising every model in the same way. For example, UML models
based on Eclipse Modelling Framework (EMF) are used to capture information
and knowledge in Sensation and Abstraction, in the same way PSM and DEM
are manipulated. UML activity diagram, soaML, State Machine, and BPMN
are used in Realisation similarly. Some models transformation techniques on
Eclipse modelling platform are also integrated in OASEF to transform various
OASEF models into different forms and generate desired system in an automatic
or semi-automatic fashion.
Moreover, OASEF provides a mechanism called Epitome to reuse engineer-
ing capacities to conduct OAP activities. It is a typical and justified means, or
capability to achieve some engineering purposes. It is generalised from proven
examples that tightly link two OAP activities together, such as a specific Re-
alisation that is able to achieve predictable and optimised results in reality to
improve identified problematic situations. Applying an existing Epitome directly
produce a previously proven outcome without having to go through the particu-
lar activities. For instance, it generate specific Realisation models that are able
to improve the typical problems.
A number of supporting tools are integrated within an integrated supporting
tools environment, called IPEOAP. It is developed using Java programming lan-
guage on top of Eclipse IDE, EMF and modelling platform. A range of graphical
model editors are developed to view, create and modify models for various OAP
activities, such as PSM and DEM.
3 Case studies
Two controlled case studies were conducted as proof-of-concept, as oppose to
proof-of-performance, which aim to assess whether, in general, its objectives are
meet in real world settings. The first case study is in the context of online travel
booking business, a typical scenario used in service community. While the second
one is based on Australian First Home Saver Accounts (FHSA) scheme that was
introduced by the federal government in 2008 to help residents to purchase their
first homes. This section briefly presents some outcomes of these two case studies
to illustrate the main feature, concepts and application of OASEF.
A PSM in the context of online travel booking exemplifies the high level
analysis of Problem Situation in Rationalisation. It contains many higher level
Purposes for online booking, such as “Ease of use”, “Quick Responsiveness”,
“Reliable booking”, ”Economic price”, and “Secure and trust”. These PSM el-
ements are generated systematically in accordance with general knowledge cap-
tured in general abstraction models within world images. It also contains a range
of higher level Problems such as “The booking process takes too long”, which
violates the “Quick Responsiveness” Purpose. This Problem is also caused by
other Problems such as “Too many service providers involved”, “Some providers
are less efficient than others”, “Insufficient network bandwidth”, ”Diversity in
interface and interaction mechanisms”, and ”The ordering process is too com-
plex” that is caused by “sequential correspondence”. Exploration of problems
and purposes reveal their nature and relationships and help to identify, under-
stand, and capture important problematic situations.
The PSM also help to systematically generate other models during successive
engineering activities. As an example, since the problem “Online travel booking
takes too long time” is identified as a major problem that affects a higher pri-
ority purpose “Quick Responsiveness”, a DEM, depicted by figure 5, is created
to reveal the best desired improvements to address the problem. The construc-
tion process of DEM is guided by, and makes referenced to, elements in the
above PSM. For example, The desired improvement of efficiency during travel
booking process includes a higher level ideal, called “Fast booking”, which is
achieved via a number of more specific Objectives, such as “Service provider
filtering”, “Caching”, ”Simplify booking process”, and “Parallel processing”,
which, respectively, filters out slow service providers, provides cache to provide
repeated information locally, simplifies the booking processes, and interacts with
service providers in a parallel and asynchronous fashion. The last Objective con-
tains some lower-level specific Goal s, such as “Parallel booking” and “Parallel
queries”. The identification of these improvement and desired ends at different
level of details helps to discover the “right” and achievable goals and desired
capabilities to address the important problems.
Fig. 5: An example of DEM in an online travel booking scenario
In OASEF, higher level models such as System Abstraction, PSM, and DEM
are used to derive and capture intellectual achievement as crucial engineering
resources. In the meantime, more concrete design is also captured by models. For
example, UML Activity diagrams are used to describe the internal structure and
behaviour of subordinate business processes in Capability Design, with the aim to
achieve desired ends in DEM. A design of a process to “Close an existing FHSA
account” is systematically derived in accordance with the identified problem of
“Cannot manage FSHS account in current bank system” in its PSM. It contains
interconnected required capabilities within an orchestrated structure, such as
“Check Eligibility”, “Acquire state information”, “Validate Customer Closure
Form”, “Fund processing”, and “Notify Customer Result”. When conducting
Service Composition during Implementation, the above Capability Design model
is automatically transformed into more concrete formalism, such as Business
Process Execution Language(BPEL) models, which can be directly executed in
a run-time environment, such as Apache ODE BPEL engine used in the case
studies. The transformation process from UML Activity Diagrams to BPEL can
utilise various BPEL transformation tools such as the MDD4SOA tools set [8].
4 Evaluation and analysis
Some observation and assessment of OASEF are made during the design and
implementation of proof-of-concept systems in real world settings. Due to a fo-
cus on, and methodological support for, higher-level intellectual efforts, OASEF
helps to analyse and understand a complex situation, and to systematically de-
rive sensible and rational business decisions, for example, in one of the case
study, whether or not provide support for a bank business under specific leg-
islation environment. The modelling mechanism and tool environment enables
developers to concentrate on exploring and understanding higher order business
issues including complex situations and desired business ends to be achieve, us-
ing models such as PSM and DEM. The graphical tools provided in IPEOAP
facilitate the manipulation of these higher-level intellectual efforts, such as design
and manipulation of Abstraction, Problem Situation, and Desired End. The use
of unified EMF modelling meta-model enables interactions and cross-references
among various OASEF activities, using either general purpose UML models or
specially designed models such as DEM.
In the meantime, “accidental complexity” of underlying implementation tech-
nologies is largely hidden during the case study due to capturing and exploitation
of engineering capacities by using Epitomes and automatic model transforma-
tion techniques. For example, the processes to create database persistence logic,
web service provision and invocation, and web user interface are completely cap-
tured in various epitomes. Consequently, given a System Abstraction model such
as information model for a flight or bank account, OASEF enables 100 percent
automatic code generation, which produces systems capable of collecting infor-
mation from users, persisting data, and providing relevant information service.
Experience from the case studies shows that, based on well captured higher or-
der models and technological capacities, generation of concrete systems at the
implementation level is relatively fast, and requires little human manipulation.
Some issues and limitations are also revealed by the case studies. Although
the proof-of-concept case studies were based on real world settings, the imple-
mented systems are not assessed in real business environment. In fact, since the
controlled case studies were designed to demonstrate and evaluate the objec-
tives of OASEF, by the same person who designed the framework, the results
are hence less convincing compared with empirical practice by third parties.
Moreover, various OASEF activities are neither monitored nor validated in the
engineering process, which makes it hard to identify problems when things go
wrong. Furthermore, due to its proof-of-concept purpose, these case studies did
not cover all aspects of the framework such as sensation and control. It hence
requires further work to improve the prototype and conduct thorough empirical
evaluation in practice, such as applying evaluation metrics to quantitatively as-
sess the effectiveness in real world projects, ideally on a benchmark system, and
in comparison with other approaches.
Based on the empirical experience from the case studies, OASEF is also com-
pared with a range of other approaches, which have varying focus, objectives,
and realisation methods. Kohlborn et. al. proposed a consolidated approach that
aims to provide a good business and IT alignment by layering them separately
with certain linkage in between [9]. For each layer, four stages, namely Prepara-
tion, Identification, Detailing, and Prioritisation, are used to progressively iden-
tify, elaborate, and provide desired services that, collectively, form the systems.
However, the nature and content of its higher order activities such as “Conduct-
ing interviews”, “Conducting capability analysis”, and “Defining domains” are
vaguely defined and lack practical guidance. Moreover, this work provides insuf-
ficient supports for capturing and managing both higher level business and lower
level technologies in a flexible fashion within its predefined layers. Its strength is
weakened in practice due to the lack of concrete formalism and modelling tech-
niques. In comparison, OASEF, founded on sounds theoretical base, defines the
scope, purpose, and relationship of its activities, and more importantly, provides
specific modelling mechanism to manage them.
Lamparter and Sure also proposed an interdisciplinary methodology that
combines a Web Service engineering method with market engineering and on-
tology that aims to coordinate services and customer in a collaborative environ-
ment [10]. Although it covers a full range of system analysis and design activities,
no specific means is provided to manage uncertainty and volatility in a dynamic
environment, which makes it less effective when changes are required in a timely
fashion. Whereas OASEF attacks this issue by allowing flexible aggregations
of previously-proven engineering capacities and process automation in a unified
environment, in accordance with captured higher order rational justifications.
There are also a number of other model-driven approaches to service engi-
neering [11, 8] that provides various modelling facilities and tools to map busi-
ness processes into executable SOS. For example, a model driven technique is
applied to build SOS by converting higher level business processes, captured in
UML activity diagrams, into executable BPEL files [8]. Some tools are devel-
oped to facilitate reasonably smooth transition from business design captured
in BPMN and activity diagrams, into system implementation [11, 8]. However,
unlike OASEF, these approaches do not focus on, and provides no support for,
some higher level intellectual efforts such as identification of problems and im-
provement. Although our previous work [7] also applied a model-driven approach
to facilitate higher level modelling through conceptual orientation and decision,
it does not realise our vision of service engineering due to some great difficul-
ties, such as a lack of clear definitions and concrete formalisms for problem-level
abstraction, and inadequate separation of various engineering concerns during
each phase.
5 Conclusion
In this paper, we propose a synthetic approach to service engineering. It incorpo-
rates some inter-related elements including a conceptual foundation and guiding
principles, a novel process model, a model-driven method, and an integrated
supporting environment.
Two proof-of-concept case studies were conducted in real world settings, and
are briefly presented in this paper to illustrate some important concepts and
techniques. The results show that this approach can be applied in real-world
settings to facilitate service engineering and achieve its design goals in gen-
eral. More specifically, the following positive results are observed. Firstly, by
using higher order models such as PSM and DEM, OASEF enables and pro-
motes coherent higher order intellectual activities, by which effective services
and systems are presupposed. Models in Abstraction, Rationalisation, and Re-
alisation, are captured as important engineering resources that can be located
and aggregated together. They hence form important human intellectual assets
that provide creative and valuable essence to achieve the “right” and optimised
systems. Secondly, activities involving implementation technologies in Action,
are also captured as reusable engineering capacities, and are used to automat-
ically realise identified desired ends in a relatively easy way. Thirdly, using a
model-driven method, OASEF creates an effective linkage between higher order
intellectual efforts and lower level implementation processes, since both type of
resources are organised in a unified, identifiable, reusable fashion. The coherently
aggregation of resources and capacities are used altogether to systemically and
automatically drive the engineering process to produce desired SOS.
Some important issues and limitations are also revealed during the prototyp-
ing and evaluation processing. Corresponding improvements and more thorough
evaluation of its practical effectiveness in real world projects are essential to
consider in future research.
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