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

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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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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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

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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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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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.

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 ISSIP Presentation

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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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.

Web service composition

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 /

Hotel Book Flight

Tourist Information

End Start

› 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-based Optimization-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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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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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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Multi-attribute Combinatorial Procurement Auction

Service Requester

Auctioneer

A B

C

D E

Items

Tasks in the Abstract composite Service

1 2

3 5 4

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 ISSIP Presentation

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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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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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

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

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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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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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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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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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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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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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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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!

Teaching and Learning Initiatives

Multi-institutional project funded by the Australian Learning and Teaching Council

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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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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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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.

Initial Modules and their inter-relationships