This document summarizes Joseph G. Davis's research at the University of Sydney related to services. It discusses (1) developing a science of services, (2) studying service level agreements through ethnographic research, and (3) modeling service interaction networks. It also touches on challenges in services research and the potential for service science and a service web to advance this area through empirical and interdisciplinary study.

1. Services-related research at the
University of Sydney
JOSEPH G. DAVIS| Professor of Information Systems and
Services
Director, Knowledge Discovery & Management Research
Group
Theme Leader- Centre for Distributed and High
Performance Computing
School of Information Technologies

2. Outline
A Science of services?
Service level agreements (SLAs): an ethnographic
study
Service Science, service web, and service
computing
- web service composition, service marketplaces
- integrating human computation, crowdsourcing
Modeling service interaction networks
Curricular initiatives in service science
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3. Can there really be a science of
services?
“Wherever there are important phenomena, there can be a
science to describe and explain those phenomena. Thus, the
simplest (and correct) answer to “What is botany?” is, “Botany
is the study of plants.” And zoology is the study of animals,
astronomy the study of stars, and so on. Phenomena breed
sciences.”
- Newell, A., Perlis, A. & Simon, H. A. (1967).
Computer Science, Science, 157, 1373-1374.
Service science as the systematic study of service systems
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4. A. Service Provider
• Organization – can
Involve multiple agents
C. Service Target:
B. Service Client
• Organization- can
• Involve multiple agents
Service relations and interactions
(co-creation of value)
Interventions of A on C
- based on Gadrey (2002)
IT-centric services
Interventions of B on C
Forms of ownership of B on C
IT-enabled interactions
and solutions

5. Challenges in Service-related Research
› Services research as inherently inter-disciplinary
› Extreme diversity in the service sector – wide variation
in their materiality and knowledge-intensity (Gallouj
2002)
› Nebulous nature of the output, difficulty in measuring
the ‘product’; perishability
› Interactive nature of service design and delivery.
(CHIP – co-production, heterogeneity, intangibility,
perishability)
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6. Service science as empirical and inter-disciplinary
› Knowledge based on observable phenomena
› must be capable of being tested for validity under a variety of
conditions (methods include modeling and simulation,
experiments, field-based methods etc.),
› embrace analytical/quantitative, computational, and
qualitative approaches
› Real world observations and data central to this
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7. Service science and service web
Service science – an attempt to develop a scientific body of
knowledge around the ‘service system’ as the primary
unit/object of analysis.
Goals
 to inform and improve the engineering and management of
complex, interacting service systems,
to support the training of service professionals
Service Web – an engineering project that can advance web
technology to enable billions of services to be exposed,
composed, consumed over the Web.

8. Service level Agreements (SLAs)
› Ethnographic study of the development, enactment,
and use of SLAs involving complex, IT-intensive
services informed by the relational theory of contract
(drawn from legal studies)
› Fieldwork completed at the shared site of a large,
global, IT-services provider and a large, global,
financial services company
› Two year lead time to get the necessary approvals
› Exploration of the ‘gap’ between the normative view
and how the customer and (multiple) provider agents
interact under emergent conditions to interpret the
contract terms and to enact interventions,
› Based on Macneil’s relational theory of contract
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9. Preliminary insights
› Under-representation in SLAs; SLAs at best partial
representations of actual work ; many important
details added over multiple iterations during the
enactment of the service over time; excessive
demands on service provider agents
› Emergence of the new virtual organisation at the
interfaces between the client and provider agents.
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10. Service web
› Involves the integration of:
 Service oriented architectures and principles to support
the development of complex services using distributed
and reusable components,
Web principles, standards, and infrastructure,
Semantic technologies for service discovery,
composition, fault tolerance, execution etc.

11. Web service composition

12. Introduction: Research Problem
Composite Service Selection
Composite service selection refers to the process of selecting web
services that can execute the BP’s required functionalities, with the
aim of choosing those services that best match service requester’s
requirements and constraints while simultaneously maximizing the
user utility in terms of the quality of service and cost.
Book
Hotel
Transfer
Airport /
HotelBook
Flight
Tourist
Information
EndStart

13. › New solution: Economically-motivated models based on
Mechanism Design and Auction Theory
Hybrid
Auction-based
+Flexible pricing model
+Requesters express their needs
Pre-determined
Not-customizable
Profile
Flexible
Negotiable
Profile
•Zeng 2004
•Yu 2007
•Wiesemann 2008
•Canfora 2005
•Ma&Zhang 2008
•Lecue 2009 •Comuzzi&Pernici 2009
•Ardagna&Pernici 2007
•Yan 2007
•Chhetri 2006
•Jiuxin 2010
•Richter 2011
Negotiation-basedOptimization-based
Optimization +
Negotiation
Optimization+
Configuration
Complicated
Decision Models
required
+ No complex decision model
+Global optimum
Service Selection Spectrum
Service Selection Spectrum *
based on the underlying assumption on QoS Profile
*MOGHADDAM, M. & DAVIS, J. 2013. Service Selection in Web Service Composition: A Comparative Review of Existing Approaches. In:
BOUGUETTAYA, A., SHENG, M. & DANIEL, F. (eds.) Handbook on Web Services: Web Services Foundations. Springer.
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14. Economically-motivated Models
› First step: one service requester, multiple service providers
A single auction
› Second step: multiple service requesters, and multiple service providers
A marketplace for web services
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15. Designing an Auction
Auction Properties
+Economic Efficiency
+Incentive Compatibility
+Revenue Maximization
+Budget Balance
+Individual Rationality
+Computational Traceability
+Pareto Efficiency
The
communication
language,
formalize the
bids
Who wins
what?
How much
should the
winner pay
(be paid)?
Winner
Determination
Problem
(WDP)
Bidding
Language
Pricing
Scheme
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16. Multi-attribute Combinatorial Procurement Auction
Service
Requester
Auctioneer
A
B
C
D
E
Items
Tasks in the Abstract
composite Service
1 2
3 54
Bidders
Service Providers
Bid over price and quality (e.g.
Availability and execution time),
and bundles of tasks
Bid provider1=
{(B,100$,97%,.03sec) OR (D,50$,97%,.05sec) OR
OR (BD,110$,97%,.04sec)}
› Combinatorial Auctions:
› Multiple distinct items simultaneously, bidding over bundles
› Dependencies between items -> Complementarity or Substitutability
› Tasks in a BP (and their corresponding services) dependent over
factors
› Service providers can internalize part of the cost and reduce price
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17. Representation of services in SOA
› I - a set of inputs
› O - a set of outputs
› P - a set of prerequisites
› E - a set of effects
› N - a set of non-functional requirements
Underlying infrastructure provided for integration of services provided by
web technologies;
Web 2.0 technologies as means to structure human-machine cooperation
Semantic technologies and ontologies for service discovery, orchestration ,
composition , and execution.
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18. Problems/Challenges
› Limited uptake beyond enterprise-specific contexts
› Poor support across the entire service life cycle (location, negotiation,
mediation, adaptation, composition, SLAs etc)
› Limited semantic support
› Critical need for augmentation through human agents – seeming failure of
the pure automation approach.
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19. Limits of pure SOA approach
› Largely concerned with the functional dimension,
› For the service web to take off, the social and semantic dimensions are
equally important – also the lesson from service science

20. EU SOA4All Project Approach – leveraging online
communities
Source : J Domingue et al. (2009) ,“The Service Web: a Web of Billions of Services” , Towards the Future Internet

21. Service Web
› Evolution of the world wide web
› Service Science and service web – two complementary perspectives
› Characterizing service ecosystems as socio-technical-economic
systems
› A vision for the future based on the notion of augmentation.
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22. Service Web Perspective
› Based on combination of semantic technologies and service oriented
computing
› Vision of billions of services exposed by providers and consumed online
› Complex services created flexibly by linking loosely coupled components
over the network
› Model based on fully automated service delivery over the web
› New business models such as infrastructure as service (IaaS), platform as
service (PaaS), virtualization, Software as service (SaaS) etc.
› Service ecosystem still evolving with new service models needed for
security, privacy, compliance, trust, verification etc
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23. Internet and the Evolution of the World Wide Web
Web 1.0
• Mainly for information dissemination, e-
commerce, web as vector of exposure, read-
only web.
Web 2.0
• Participative web, read-write web, user-
contributed web
Web 3.0
• Service web, read-write-execute web,
semantic web
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24. Human Agency
› Need to weave human agency and semantics seamlessly into the service
web along with other resources such as content, (web) services, and
devices,
› Exploit the human’s unique and complementary capacity to mediate
between services, achieve effectiveness-linked QoS measures
› Achieving a balance in complementary service provision by humans and
machines, mixed-initiative services
› Many unresolved issues:
- Description
- Synchronicity
- Scalability
- .....many others
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25. Logic of Crowdsourcing
› Harnessing the combined power of computers and human intelligence to
solve complex problems that are beyond the scope of existing AI
algorithms (typically involving conceptual thinking, perceptual skills etc.)
› Problems that generally defy closed system solution
› Opportunity to leverage the abilities of large number of people made
possible by the Internet and the World Wide Web.
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26. Human Computation and Crowdservicing
› Human agents provide all or part of a badly needed service, typically in
combination with one or more computational services.
› Balanced integration of diverse services provided by the machines and
human agents over the world wide web,
› General assumption – the ‘augmentation’ provided by human computation
can produce better results (than either the machine or the human regime)
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27. Crowdsourcing (microtasking)
› On-demand global workforce completing short tasks online
› Who logs on to complete microtasks?
- Millions of workers available online at any time from
› Who can create tasks for workers?
- Anyone (on many platforms, Amazon Mechanical Turk or
Crowdflower)
› What kind of tasks can you create?
- Breakdown the task into micro human intelligence tasks - anything
embeddable in a browser or phone – programmatic interfaces
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28. Service science and service web
› Service science and service web are both work-in-progress,
› Both have the potential to contribute to the other
› Service science needs to move to the next step (beyond definitions and
central concepts to trans-disciplinary theorising and empirical research
based on robust theories)
› Service web needs to make progress on the social and semantic
dimensions!

29. Teaching and Learning Initiatives
Multi-institutional project funded by the Australian
Learning and Teaching Council
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30. Participating Universities
› University of Sydney (Lead Institution)
› University of New South Wales
› University of Queensland
› University of Melbourne
(15 researchers in all)
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31. Primary Goals
› Establish an educational consortium to research and
develop a model of innovative PG education to reflect the
importance of the service sector to the global economy,
› Research the key knowledge and skill sets needed by IT
professionals,
› Create a broad framework and develop appropriate
curriculum modules and a range of teaching materials
› Create an service science education portal coupled with a
‘services foundry’ (to facilitate agile software development)
› Raise the profile of service science-related teaching and
research in Australia
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32. Recurring themes in focus groups
› Customer behaviour and motivation
› Learning with customers
› Virtual teams/organisations, inter-
enterprise services, value chains,
networks
› Communication competence, virtual
project management
› Governance and management
› Resourcing issues, outsourcing
› Data analytics, dashboards, data
mining
› SOA technologies and standards
› Service systems lifecycle, agile
development
› Process view, business process
modelling, management , process
standards.

33. Initial Modules and their inter-relationships